计算机科学辑要 Compendium of Computer Science ISSN Print: 3079-5362 ISSN Online: 3079-5370
Wood Defect Detection Algorithm Integrating Multi-Scale Features
This paper proposes a lightweight wood defect detection model, termed MADORE-Minify, which effectively addresses the limitations of existing methods in balancing multi-scale target representation and parameter redundancy in wood defect detection. The overall architecture of MADORE-Minify follows the YOLO11 framework and is further optimized and enhanced upon this baseline. First, the backbone network integrates the Mixed Local Channel Attention (MLCA) module to strengthen feature extraction capability, enabling more effective detection of small-scale defects while al-leviating semantic information loss for large-scale targets. Second, the ADown downsampling module is employed to replace the upsampling layers, enhancing the model’s ability to capture features at different scales, particularly in preserving fine details of small targets. Meanwhile, this design reduces the spatial resolution of feature maps, thereby significantly decreasing computational cost and parameter complexity. Finally, an OREPA-GENet module is designed, which transforms nonlinear blocks into linear ones and com-presses complex structures into a single convolutional layer during the inference stage, achieving substan-tial model lightweighting and efficiency improvement. Extensive experiments conducted on a public wood defect dataset demonstrate the superiority of the proposed MADORE-Minify algorithm. Compared with several competing methods, MADORE-Minify achieves a better balance between detection accuracy and model lightweighting. In addition, the proposed model attains high detection accuracy with only 6.6M parameters, validating its outstanding performance and lightweight characteristics in the field of wood defect detection.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
SAM2-FNet: A Frequency-Domain Mul-ti-Expert Fusion Network for Medical Image Segmentation
In recent years, deep learning has achieved substantial progress in medical image segmentation; however, existing methods remain constrained by the challenge of effectively coupling local detail with global semantic context. To address this issue, we propose SAM2-FNet, a frequency-domain expert-fusion network built upon an improved SAM2-UNet. SAM2-FNet attains fine-grained, multi-scale feature enhancement through frequency-domain decoupling and a multi-expert collaborative mechanism, leading to marked improvements in segmentation accuracy.Specifically, SAM2-FNet provides three primary capabilities:(1) Frequency-domain decoupling and enhancement— input features are decomposed into high-frequency and low-frequency components via a two-dimensional Fourier transform; the resulting components are processed by a dual-branch heterogeneous pipeline and adaptively fused using channel-wise attention to preserve both texture details and global structure.(2) Dynamic expert decision-making — an improved Expert Fusion Module (FEM) implements a multi-expert architecture composed of five sub-networks (SubNets), enabling dynamic, data-driven expert weighting and enhancing the model’s generalization to lesion heterogeneity.(3) Decoder optimization — a Local Refinement Module (LRM) is introduced in the decoder: an atrous (dilated) convolutional pyramid combined with a spatial-attention mechanism narrows the encoder–decoder semantic gap and improves boundary reconstruction fidelity.Experiments on the Kvasir-SEG dataset demonstrate that SAM2-FNet yields a 3.0% absolute increase in Dice coefficient and a 4.1% absolute increase in IoU relative to the baseline SAM2-UNet. Moreover, compared with benchmark models such as umambabot and SwinUNETR, SAM2-FNet exhibits superior robustness under high-noise and low-contrast imaging conditions
PPMA-YOLO: A Method for Metal Surface Defect Detection
Metal sheets are vital to core industries like aerospace, automotive, and bridge construction, where their quality directly determines the safety and lifespan of final products. During production, fac-tors like material impurities and equipment wear often cause surface defects such as stains, scratches, and cracks, which can lead to structural failure and corrosion. As manufacturing stand-ards rise, detecting these defects has become essential for maintaining quality and optimizing production processes. To address this, this paper introduces a detection method called PPMA-YOLO. We first replaced the standard YOLOv8 backbone with a lightweight PP-LCNet struc-ture to make the model faster and more efficient, using the H-Swish activation function and adding an SE attention module with large-kernel convolutions to balance precision and speed. In the neck section, we improved the Multi-Contextual Attention (MCALayer) and combined it with multi-scale feature fusion and small residual blocks to create the MAFR module. This module, embedded in the detection head, helps the model capture refined features while avoiding common training issues like gradient explosion. Finally, we updated the SIoU loss function with an adaptive weight and a shape-matching cost, allowing the model to better locate defects of various sizes and shapes. Tests on the NEU-DET dataset show that our method is highly effective; with a model size of only 4.8 MB, it achieved an 83.87% mAP@0.5. Compared to existing methods, PPMA-YOLO is both more accu-rate and faster, making it an ideal solution for real-time defect detection in industrial settings.
Ontology Construction Methods for the Tourism Domain Oriented to the Semantic Web
With the accelerating transformation of the tourism industry toward intelligent services, achieving semantic integration and efficient organization of heterogeneous tourism data has become a critical issue. Semantic Web technologies provide a solid foundation for ontology construction in the tourism domain, enabling the development of structured and machine-interpretable knowledge systems. This paper presents a comprehensive review of tourism ontology construction methods, covering key concepts, ontology types, and construction workflows. It further analyzes both manual and (semi-)automated approaches, compares mainstream ontology engineering tools, and summarizes representative case studies. The study highlights major challenges such as data heterogeneity, ontology consistency, and cross-cultural adaptability. Finally, the paper explores future directions including ontology learning, integration with knowledge graphs, large language model-assisted construction, and the application of graph neural networks. The findings aim to support researchers and practitioners in designing more adaptive, scalable, and intelligent semantic frameworks for tourism information systems.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
Personalized Tourism Recommendation Algorithms Oriented to the Semantic Web
With the explosive growth of tourism data and increasingly diverse user demands, traditional recommender systems face limitations in addressing coldstart issues, semantic understanding, and explainability. This study provides a systematic review of personalized travel recommendation approaches oriented to the Semantic Web, with a focus on ontology-based user modeling, semantically enhanced recommendation algorithms, and ruledriven inference mechanisms. The findings highlight the advantages of semantic technologies in knowledge representation and explainable reasoning, particularly in integrating heterogeneous data and deriving complex user interests. The study also outlines future research directions combining large language models and graph neural networks to ad-vance intelligent recommendation systems.
Architecture and Implementation of Semantic Web-Based Tourism Recommender Systems
Traditional tourism recommender systems face limitations in processing heterogeneous data, capturing users' semantic preferences, and delivering explainable results. This study explores a semantic Web-based architecture for tourism recommendation systems, highlighting a layered model driven by ontologies. The proposed architecture comprises four key layers: RDF-based data integration, OWL-based ontology modeling, semantic reasoning, and service-oriented applications. Comparative analysis of reasoning engines (Jena, Pellet, Hermit) and real-world systems (e.g., SWTOUR) reveals critical challenges such as reasoning complexity, ontology construction costs, and difficulties in user semantic modeling. The study further outlines future directions incorporating microservice architecture, edge computing, and lightweight semantic services to enhance system scalability and intelligence.
Semantic Integration and Annotation Techniques for Tourism Data Based on the Semantic Web
As culture and tourism integration accelerates, the tourism industry is increasingly challenged by heterogeneous data sources, inconsistent structures, and semantic fragmentation. Semantic Web technologies-through ontology construction, RDF modeling, semantic mapping, and logical reason-ing-offer a knowledge-driven approach to data integration and annotation. This paper comprehen-sively surveys key techniques for semantic tourism data integration and annotation, including data extraction, transformation, ontology alignment, and conflict resolution. It contrasts rule-based, ma-chine learning, and deep learning annotation paradigms, and evaluates mainstream tools for struc-tured and unstructured tourism data processing. The study further identifies technical bottlenecks in scalability, data quality, and semantic drift, proposing future directions such as LLM-assisted semantic annotation, incremental ontology evolution, and privacy-preserving reasoning. This research aims to provide a technical foundation and strategic reference for intelligent tourism systems and semantic data governance.
Optimization of Tourism Recommender Systems Through User Behavior Analysis
With the deep integration of digital tourism and intelligent recommendation, traditional tourism recommender systems face significant limitations in interpreting multi-source user behavior, semantic preference extraction, and explainable recommendation generation. This study presents a com-prehensive review of user behavior modeling approaches, classifying explicit and implicit behavior types and highlighting the semantic Web's advantages in behavior abstraction. A layered recommen-dation architecture is proposed, integrating RDF/OWL ontologies, reasoning engines, and behavioral intent graphs. Context-aware, real-time, and deep learning-based methods are compared in terms of accuracy, latency, and interpretability. Addressing challenges in system scalability and user privacy, the study outlines future directions in GNNs, reinforcement learning, federated profiling, and multi-modal behavior fusion. The findings suggest that semantic-enhanced behavior analysis is pivotal for building intelligent, trustworthy, and sustainable tourism recommender systems.
A preliminary study on the application of machine learning model in shale gas frac-turing
With the rapid development of artificial intelligence technology, machine learning models have shown great potential in many fields. This paper focuses on the application of machine learning model in shale gas fracturing, starting from the complexity of shale gas fracturing and the limitations of traditional methods, expounds the application scenarios, advantages and challenges of machine learning model in this field, demonstrates its practical application effect through case analysis, and finally discusses the future development direction, aiming to provide reference for the optimization and innovation of shale gas fracturing technology.
A Study on Remote Sensing Image Sharp-en-ing Using Graph Proxy Attention
In recent years, as a technology that simulates the selective focusing ability of the human visual system, attention mechanisms have gained significant attention in the field of image processing. Within this field, Transformer architectures based on self-attention mechanisms show great po-tential for effectively addressing issues related to long-range dependencies and global feature modeling. Nevertheless, the high computational complexity associated with self-attention mechanisms has restricted their application. This paper introduces a novel attention mecha-nism—Graph Proxy Attention. It is applied in the remote sensing image sharpening model called HyperTransformer. Experiments are carried out on the public Pavia Center dataset. The im-proved model is evaluated in terms of computational efficiency and image sharpening perfor-mance. The results indicate that, compared to models based on self-attention and axial attention, the Graph Proxy Attention model significantly reduces the number of parameters and float-ing-point operations. Specifically, when compared to self-attention models, the parameter amount is reduced by 21.0%, and the floating-point operations are reduced by 22.6%. In com-parison to axial attention models, the parameter amount is reduced by 13.4%, and the float-ing-point operations are reduced by 15.7%. At the same time, the evaluation indicators for im-age sharpening quality have also achieved favorable results. For example, the relative global synthetic error has dropped to 0.48. These findings demonstrate the applicability of the Graph Proxy Attention mechanism in remote sensing image sharpening.
PrimLock: Leveraging Linear Programming to Optimize Primitive Logic Locking
This paper presents PrimLock, a logic locking framework that employs integer linear programming (ILP) to optimize key gate placement in integrated circuits (ICs), addressing vulnerabilities like IP theft and hardware Trojans in untrusted supply chains. Existing methods suffer from heuristic key placement, scalability limitations, and susceptibility to SAT attacks. PrimLock introduces a du-al-circuit ILP model that maximizes output corruption under incorrect keys while minimizing computational overhead. A standardized security metric quantifies resilience as the percentage of corrupted outputs, validated on the C17 benchmark. Results show 100% corruption in critical test cases, outper-forming heuristic and SAT-resistant approaches. Constraint reduction tech-niques reduce computational complexity significantly, enabling scalability to industrial designs. This work establishes a mathematically rigorous foundation for logic locking, balancing provable security with practical efficiency.
Analysis of EU Artificial Intelligence Policy
In the race for leadership in emerging technologies typified by artificial intelligence (AI), the European Union (EU) stands out as one of the pioneering regional cooperation organizations to embark on AI safety governance[1].With digital technologies becoming increasingly politicized and securitized, the dynamics of competition and cooperation in the digital sphere are more and more shaped by security considerations. Consequently, the EU's emphasis on digital security governance has progressively pivoted from data protection towards the establishment of digital sovereignty[2]. Amidst the evolution of AI policies from soft law to hard law, the EU is poised to reassert the Brussels effect, enhancing the application of digital security governance in AI and other realms, fostering the creation of a more cohesive policy framework and maximizing the realization of the European digital single market strategy. Against this backdrop, China should ground in its own realities, adopt a dialectical approach to appraising the merits and demerits of the EU's AI legislation, assimilating the valuable insights, and prudently deliberating on the trajectory for China's future AI governance.
Artificial Intelligence Security Governance
Research on partition method of bearing circumferential surface based on machine vision
As a key component of industrial machinery, the surface defects of bearings will directly af-fect the safety of industrial production, so it is of great significance to carry out quality inspec-tion of bearings in actual industrial production. Because the defects in different positions on the circumferential surface have different damage degrees to the bearing, the defects in the core area of the circumferential surface are the most harmful. Therefore, based on machine vision inspection, which is the mainstream apparent quality inspection technology of industrial prod-ucts. The essence of the bearing circumferential partitioning task is to accurately extract the up-per and lower edges of the bearing in the image. In order to solve the problems of uneven image illumination, oil stain and bump at the bearing edge in the bearing edge extraction task, this the-sis proposes a bearing demarcation line detection algorithm based on adaptive Butterworth homomorphic filtering and multi-scale Gaussian line fusion. Firstly, to address the problem of uneven bearing illumination, this thesis proposes an image adaptive threshold segmentation al-gorithm based on Butterworth homomorphic filtering. The Gaussian filter in the homomorphic filter is replaced by the Butterworth filter, which effectively improves uneven illumination. Then, in combination with the adaptive threshold segmentation algorithm, the appropriate parameters were automatically selected to preprocess the image and enhance the edge feature information. Subsequently, to eliminate the influence of defects, this thesis proposes a demarcation line coarse localisation algorithm based on a class-aware hashing algorithm. The algorithm auto-matically locates near the bearing demarcation line and selects the appropriate region of inter-est to minimise the false edge problem caused by defects. Finally, to address the issue of bearing demarcation line fracture, a multi-scale bearing edge detection algorithm based on Gaussian line is proposed. The algorithm accurately extracts the coordinates of the dividing line by fusing the convolution map of multi-scale Gaussian derivative images, and uses the least squares method for curve fitting. Through the above method, the accuracy of the final bearing partition reached 99.23%.
Intelligent Production Decision-Making System for Injection Molding Factories
This paper addresses the intelligent transformation needs of the traditional injection molding industry by proposing a comprehensive intelligent production decision-making system integrating smart scheduling, auxiliary decision-making, and digital twin simulation. By combining the Weighted Shortest Processing Time (WSPT) algorithm, dynamic programming, and equipment health assessment, the system achieves dynamic multi-machine scheduling and resource optimization. An injection molding industry-specific large model is constructed, leveraging real-time data analysis and a knowledge base to optimize process parameters and enable anomaly early warning. A digital twin platform is developed using Unity and OPC UA protocols, employing multi-physics simulation and Markov decision processes to predict production issues. The research provides a practical technical solution for the intelligent upgrading of injection molding factories, offering both theoretical innovation and engineering application value.
Injection Molding Production Optimization
Language Lab Construction: Exploring the Integration of Philosophy, Technology and Educational Value
The paper discusses in depth the integration of philosophy, technology and educational value in the construction of language laboratories. It analyzes the construction concepts of stu-dent-centered, information technology-driven, and composite talent cultivation, elaborates on the construction technologies such as hardware facilities and technical performance require-ments, and reveals their educational value in improving teaching effect, cultivating self-learning ability, and promoting resource allocation. It emphasizes the in-depth integration of establishing an information management system, integrating teaching resources, and perfecting the opera-tion mechanism, providing theoretical basis and practical guidance for the optimization and ef-fective application of language laboratories.
Application Status and Development Trends of Intelligentized Technologies in Shale Gas Fracturing Production
Shale gas, as a pivotal resource in the global energy transition, has its development efficiency and cost control heavily dependent on the intelligent upgrading of fracturing technologies. This paper systematically examines the current applications of intelligent technologies in shale gas fracturing operations, identifies prevailing challenges, and outlines future development trajectories within this technical domain.
Convex Nonnegative Matrix Factorization on Bipartite Graph
In this paper, we propose a novel convex nonnegative matrix factorization (CNMF) method to learn a bipartite graph with exactly k connected components, where k is the number of clusters. The new bipartite graph learned in our model approximates the original graph but maintains an explicit cluster structure, from which we can immediately get the clustering results. Besides, inspired by self-expressive assumption of the data and subspace clustering, we added additional constraints to further recover global structural information of the data. Multiplicative updating rules are developed. The superiority of our method over the effectiveness and efficiency is demonstrated by extensive experiments.
MDA-Net: Multi-Dimensional Attention Network for Retinal Vessel Segmentation
Fundus images play an important role in the diagnosis and treatment of ophthalmic diseases (such as hypertension, arteriosclerosis and diabetic retinopathy), and the morphological infor-mation of retinal blood vessels can be used as an important index for the diagnosis of these dis-eases, so it is very important for accurate segmentation of retinal blood vessels. With the con-tinuous development of computer technology, deep learning method provides a new idea for medical image segmentation. Due to the complex structure and different scales of retinal blood vessels, the existing U-Net model still faces significant challenges in dealing with these tasks. To solve these problems, we propose a retinal vascular segmentation network MDA-Net based on multidimensional attention mechanism. Based on the U-Net structure, this method optimizes the network design by reducing the number of encoder-decoder layers to three layers, and intro-duces the Coordinate Grouped Feature Fusion (CGFF), multi-dimensional feature enhancement (MDFE) and multi-scale convolution enhancement (MSCE). Firstly, CGFF module integrates mul-ti-scale features by grouping convolution and multi-dimensional pooling, which improves the adaptability of the model to uneven distribution of blood vessels. Secondly, MDFE module com-bines channel shuffling, multi-dimensional attention and pooling operation to enhance the ex-traction ability of micro-vessel features, especially in low contrast and complex back-ground.Experimental results show that the accuracy (ACC), sensitivity (SE) and specificity (SP) of this method DRIVE 0.9825, 0.9842 and 0.9895, 0.8211, 0.8342 and 0.8452 respectively, and 0.9840, 0.9872 and 0.992 respectively. MDA-Net proposed in this paper provides a new idea and scheme for improving the performance of retinal blood vessel segmentation.
Global-Local Latent on Learning Model for Temporal Knowledge Graphs Based on Graph Neural Networks
Temporal Knowledge Graphs (TKGs) are an effective method for representing inter-entity and inter-event relationships in continuous time. The inference of temporal knowledge graphs refers to the completion of missing information for already existing points in time, and the extrapolation is the prediction of unknowable facts occurring in the future, and these two kinds of reasoning have important practical value in various fields. However, in the reasoning of temporal knowledge graphs, the lack of capturing potential relationships between entities between different times or within the same time when encoding local and global historical information has led to models that often perform poorly in the face of complex political events. To address these issues, this paper proposes a global-local latent relationship learning model for temporal knowledge graphs based on graph neural networks. Specifically, the model employs a latent relationship learning module to mine and exploit latent relationships within a time slice with multiple adjacent time slices to better simulate and interpret local and global historical information, enhancing the model's ability to deal with complex real-world event problems. Experimental results on four benchmark datasets show a 3 to 4 percentage point improvement in the model's inference capability compared to the state-of-the-art inference model.
Surface Defect Detection of Corrugated Sheets Based on Photometric Stereo Vision
Plate heat exchangers are extensively employed in industrial processes for thermal energy transfer and waste heat recovery. The core component, the cor-rugated sheets, possesses a complex metallic surface geometry that is critical to the exchanger's performance. Corrugated sheets are formed through die stamp-ing, during which manufacturing defects may occur due to process limitations. Detecting defects prior to use is crucial for ensuring the safety of industrial op-erations. Due to the complex curved surface geometry of the corrugated sheets, images captured by a single-viewpoint imaging system often suffer from shading caused by the plate’s undulating structure. As a result, defects tend to appear within shadowed regions of the surface, making it difficult to enhance their con-trast through illumination alone, thereby posing significant challenges for defect detection. The paper, based on photometric stereo vision, develops a photomet-ric stereo imaging system tailored for corrugated sheets and proposes a feature map extraction method based on Gauss spherical distance mean convolution. Based on the Gauss spherical distance feature map, defects are enhanced, and defect detection is performed using a method that fills the gray-scale concave segments of the feature map and marks the extrema points. Experimental results show that the defect detection accuracy in both the flow guide and heat ex-change regions of the corrugated sheets exceeds 95%.
A Keyframe Optimization Method Based on Information Entropy
In response to the issue that the VINS-Fusion algorithm primarily relies on heuristic strategies based on parallax magnitude and feature tracking quality during keyframe selection, making it difficult to effectively measure global map uncertainty and potentially leading to insufficient lo-calization accuracy, this paper proposes a keyframe selection optimization method based on in-formation entropy theory. First, by using a quadrotor UAV as the hardware platform, the key processes of front-end feature tracking and back-end sliding-window optimization in VINS-Fusion are systematically analyzed. Then, during the keyframe insertion stage, an infor-mation entropy metric is introduced to accurately characterize the uncertainties of map points and pose estimates. By setting an entropy threshold, the timing of keyframe insertion is adap-tively optimized, effectively reducing the global cumulative error. Finally, the proposed method is validated on the open-source EuRoC dataset and comprehensively compared with the original VINS-Fusion algorithm. Experimental results demonstrate that incorporating information en-tropy into the keyframe selection strategy significantly reduces redundant keyframes, decreases map-point uncertainty to some extent, enhances global trajectory estimation accuracy, and has only a minimal impact on the real-time performance of the algorithm.
Image of FPC Defect Model Based on Global and Local Feature Fusion
Aiming at the circuit defects caused by the manufacturing process problems of Flexible Printed Circuits (FPCs), a FPC defect image repair model based on global and local feature fusion is proposed. The model uses the Context Encoder (CE) as the framework, and com-pletes the basic repair within the defect area of the FPC by referring to the context infor-mation around the defect area of the FPC; then, by analyzing the local information repair details of the defective part, the conclusion is drawn that the receptive field is expanded, and the dilated convolution is used instead of the original ordinary convolution to im-prove the ability to extract local information. Then, we extracted global features from the entire FPC image, analyzed the prior information structure of the FPC image that is more consistent with sparsity, and integrated this information structure into the global feature extraction process to enhance the consistency and coherence of global information. In this way, the extracted local information and global information are fused to jointly guide the repair results to be more efficient. Finally, we conducted qualitative and quantitative experiments using existing popular methods and the method in this paper, respectively, and proved the effectiveness of the method in this chapter for large-scale defect repair from many aspects, meeting the requirements of repairing FPC image defects in actual industrial scenarios.
Social Structure-Based Methodology for User Identity Matching Across Social Networks
Cross-social-network user matching technology can identify the same individual across different social networks, enabling cross-network data integration. This allows for precise user profiling, which facilitates applications such as user recommendation and influence maximization. In such tasks, the social structure of users plays a critical role. However, due to the vast and complex nature of social network data, issues such as data sparsity and heterogeneity across net-works arise. Additionally, as the depth of network layers increases, extracted features tend to degrade and become homogeneous. To address these challenges, this paper proposes a Social Network Structure-based User Matching (SSUM) method. The approach introduces a subgraph structure extraction module, which, combined with the original network topology, is fed into a Transformer layer to further extract global features. This mitigates over-smoothing and over-squeezing caused by excessive layer depth, thereby obtaining vector rep-resentations of user topological structures. Subsequently, word-level vectors of user information and text-level vectors of user-generated content are extracted as user attribute features. These are concatenated to form the final user embed-ding representation. User matching is then transformed into a ranking problem by calculating Manhattan distances. Furthermore, to enhance model perfor-mance, user relationships in the dataset are pre-completed. Experimental results on real-world social network datasets demonstrate that the proposed method effectively extracts user features, improves matching accuracy, and outperforms comparative models.
Design and Implementation of AI-infused STEAM Curriculum in Junior High School
With the increasing popularity of AI courses, teachers need to constantly innovate their teaching philosophies and methods, emphasizing the cultivation of students' comprehensive qualities. In the teaching of AI courses at the junior high school level, teachers continuously reform and innovate subject teaching based on the STEAM concept, providing high-quality education and teaching for students, optimizing their learning experience, further meeting their learning needs, effectively solving problems encountered in the learning process, enabling better learning of AI knowledge, continuously developing their comprehensive abilities, and constructing efficient information technology curriculum teaching in junior high school.
Motivation Mechanism and Practical Strat-egies for Young Scientific Researchers to Carry Forward the Spirit of Scientists
Young scientific researchers are the main force in China's scientific research, and carrying forward the spirit of scientists holds great value in promoting China's scientific progress. This study elaborates on the rich connotations of the spirit of scientists, including patriotism, innovation, truth-seeking, dedication, collaboration, and education, and discusses its significant value in inspiring scientific workers to reach new heights, promoting the sustained and healthy development of the scientific and technological cause, and enhancing the country's scientific and technological competitiveness. By analyzing internal and external motivational factors such as personal ideals, career aspirations, national policies, and social atmosphere, this study explores their interactions and the pathways through which they influence the growth of young scientific researchers, thereby constructing a motivation mechanism. Furthermore, it proposes practical strategies to assist young scientific researchers in carrying forward the spirit of scientists, including strengthening education and training, setting up role models and exemplars, providing resources and support, and promoting exchanges and cooperation. The aim is to provide solid theoretical support and practical guidance for the sustained and healthy development of China's scientific and technological cause.