This paper proposes knowledge representation based on knowledge graph embedding (TransE model) and based on deep wandering (DeepWalk model) to enhance the level of intelligent recommendation of knowledge points. Synthesize and construct a knowledge graph-based Civic Education model. Analyze the node centrality specifics of the model. Carry out a controlled experiment of model application and investigate student satisfaction on this basis. The three nodes with the highest node centrality are “life view and values”, “morality and law” and “patriotism and nationalism”. The average score of the test questions in the experimental class is 71.25, and the correct rate of the six types of test questions is higher than that of the control class. Most of the students’ satisfaction level with the intelligent teaching mode combined with the model was between 65 and 100 points. 92% of the students found the teaching mode interesting at a level between (75,100]. 90% of the students’ content mastery satisfaction level was between 85 and 100 points. Intelligent teaching using the knowledge graph-based Civics education model can help students improve their interest in learning Civics knowledge and construct Civics knowledge system.

English children’s literature has strong application value in educational content selection. This study takes classic English children’s literature texts as the research object, and constructs a semantic theme mining model based on the implicit Delicacy Distribution (LDA). Through keyword weight analysis and theme probability distribution calculation, multi-dimensional theme clustering and visual characterization of literary works are realized. According to the 2378 English children’s literature collected in the corpus, the LDA model was used to extract five core themes: “Adventure and Fantasy”, “Friendship and Teamwork”, “Growth and Self-Identity”, “Family and Affection”, and “Nature and Animals”.A semester-long controlled experiment was conducted with third-grade students in an elementary school in Guangdong Province, designing graded English teaching content based on the results of topic distribution. Through the questionnaire survey, vocabulary test and reading ability assessment, it was found that students in the experimental group significantly outperformed the control group in terms of active interest in learning (12.42% increase in mean value) and independent learning ability (15.67% increase in test scores) (p<0.05). The study shows that the educational content adaptation method based on the LDA theme model can effectively optimize the selection strategy of teaching resources, and provide a theoretical basis and practical path for the precise matching of literary themes and cognitive development stages in children's English teaching.

In this paper, finite element analysis is applied to the mechanical characterization of the foot. A finite element simulation model of the foot is constructed and its material properties are defined. Finite element analysis is applied to calculate the stresses on various tissues of the foot under different touchdown modes. Set up controlled experiments to verify the advantages of FEA technology in sports. The material property values of each tissue in the simulation model differed greatly, which was in line with the actual situation of biological tissues. In the 2 touchdown modes, the change curves of flexion and extension angles of the supporting foot were generally similar in the latter 75% of the supporting phase, and the differences were concentrated in the first 25%. The movement of the foot on the coronal plane showed a general tendency toward eversion. There were 2 peaks in the vertical ground reaction force variation in the heel-touch mode and only 1 peak in the non-heel-touch mode. The resistance impulse and power impulse ratios varied widely. The time of occurrence of the maximum contact stress on the talo-heel joint surface varied. P<0.05, the experimental group was better than the control group in terms of skill level, learning interest and initiative of the two groups of students after the experiment. The use of finite element analysis to assist physical education teaching can enhance students' enthusiasm and skill level.

The automation system is gradually applied to many fields because of its intelligent and efficient characteristics, and its energy control makes the equipment work in the optimal efficiency zone, however, the actual control effect needs to be further optimized. This paper explains the energy control problem of automation system for its control process, and uses the weighted residual value method to transform the original system into a system dynamics model. On the basis of this model, the optimal control is solved by the variational method, and the energy control algorithm based on the variational method is built by combining Lie algebra. The algorithm of this paper is used to establish the energy optimal control strategy and simulation experiments are carried out as a prerequisite for constructing the driving cycle. In the simulation experiments, the energy optimal control strategy based on this paper’s algorithm saves 4.77% of fuel, which shows that the energy control of the automation system under this paper’s algorithm is better and in line with the environmental protection needs.

Graph neural networks are widely used in educational research, and have strong application potential in the prediction of students’ comprehensive development and recommendation of personalized educational resources. In this paper, the information and characteristics of students are mined from massive learning data, and the prediction method of multi-topology graph neural network is used to realize the effective prediction of students’ comprehensive development. Through the graph neural network, knowledge graph and cluster search algorithm and other technologies, the personalized learning path planning and optimization are completed, and the personalized learning path is designed. The research shows that the data accuracy of the student development trend prediction model in this paper reaches the qualifying value of 0.1, and the absolute maximum value of the error does not exceed 0.17, so the model constructed in this thesis is effective and robust. It can fulfill the task of student development direction prediction. The usage frequency of generating learning paths are more than 60%, so the learning path generation method proposed in this paper is practical. And the average grade of the users who use this method is 6.17 points higher than the average grade of the users who do not use this method.

Industrial Internet based on distributed computing and cloud computing platform forms a “cloud-edge-end” cooperative system. Facing the problem of computing task offloading for machine-type communication devices in industrial Internet scenarios, this paper transforms the task offloading problem into a Markov decision process problem, proposes an online task offloading algorithm based on deep Q neural network (DQN), and designs an optimal scheduling method based on iterative optimization for industrial Internet resources. Simulation experiments are conducted by comprehensively considering the network environment and server state during the task offloading process, and compared with other resource optimization scheduling strategies. The results show that the DQN algorithm converges in about 9000 steps and has good convergence performance. The offloading strategy based on the DQN algorithm can effectively reduce the delay, energy consumption and total overhead of the computational task offloading system in the economy.

Currently, digital libraries face challenges in piracy and illegal distribution, data and privacy security, digital content identification and traceability. In this paper, we design a blockchain-based copyright protection system for digital libraries to provide true and reliable digital copyright information for libraries and users, and to ensure the security of data information stored in the digital copyright registration system. Firstly, we classify blockchain and analyze in detail the three core technical principles of consensus mechanism, cryptography principle, and hash algorithm. Then design the copyright registration protection system that contains the functions of unique authentication of digital work copyright, IPFS distributed storage, and privacy data encryption. The designed algorithm is tested for performance and the service performance of this paper’s scheme is analyzed in real applications, and it is found that the throughput performance of this paper’s algorithm when the number of nodes ranges from 4 to 20 is on average 36.19% more than that of the PBFT consensus algorithm, and 55.92% more than that of the RBFT consensus algorithm. When there are 5000 digital resource feature vectors in the system database, the time required for similarity retrieval is only 0.523s, which meets the requirements of the system’s non-functional needs for similarity retrieval runtime, and realizes a good balance between the operational efficiency of digital libraries and security. The research has practical reference significance for the application of blockchain technology in the field of digital copyright protection.

Acupuncture has been recognized by more and more experts as a treatment method to relieve various pains in human body, but the association between specific acupuncture treatments and diseases is still unclear, which affects the long-term development of acupuncture treatment. In this paper, we abstract the knowledge of acupuncture points as ontologies in the knowledge graph, and propose a method to improve the RoBERTa-WWM-BiGRU-CRF model to optimize the knowledge extraction of the knowledge graph by combining the SoftLexicon technique and the adversarial training method. Based on the knowledge graph of acupuncture points, the collaborative filtering model is introduced, and the original similarity matrix construction method is replaced by the co-occurrence matrix construction method based on the association characteristics of acupuncture points and diseases, which improves the operational efficiency of the association search and realizes the design of the association search technology of acupuncture points and diseases. The average consultation time in the acupuncture outpatient departments of the experimental and control groups applying this paper’s technology for acupuncture visits was faster than that of the full outpatient clinic by 0.32 min, showing a significant difference (P<0.05). Patients in the experimental group who received acupuncture treatment assisted by the technology of this paper were higher than those in the control group in the dimensions of acupuncture treatment experience, such as physiological reflections, treatment emotions, and treatment effects and treatment feeling dimensions, which were 2.22, 3.57, 2.2, and 1.33, respectively.

In-depth investigation of the combination of innovation and entrepreneurship education and computing technology is of great theoretical and practical significance for the continuous promotion of innovation and entrepreneurship education in colleges and universities. In this paper, after clarifying the three elements of environment, subject and behavior in the design of innovation and entrepreneurship education courses, we design an innovative teaching model for innovation and entrepreneurship education courses based on computing technology and digital learning environment, and adopt similarity metrics and questionnaires to count the frequency of students’ on-line learning behaviors and the level of cultivation of their innovative spirit and ability respectively. The results of teaching practice show that the practice of innovation and entrepreneurship education courses based on computing has a facilitating effect on the cultivation of students’ innovative spirit and ability. The Spearman correlation coefficients of the learning behaviors in the online teaching platform of innovation and entrepreneurship education courses and the dimensions of innovation spirit and ability show medium-high correlation (r>0.3), and its regression model can effectively explain more than 60% of the variance of innovation spirit and ability. The research in this paper provides an effective reference for the innovative development and practice of innovation and entrepreneurship education programs, and lays the foundation for promoting more effective and innovative development of dual innovation education in colleges and universities.

Natural language processing (NLP) is developing very rapidly in the field of artificial intelligence, and has become an important direction in the development of computer science field and artificial intelligence industry. In this paper, in order to realize the efficient construction of natural language processing model in low-dimensional embedding space, firstly, a word vector learning model is constructed based on matrix decomposition for word vectors in natural language processing. On this basis, in order to further realize the efficient construction of natural language processing models, this paper designs the Semantic Discarding Network (SDN) and Semantic Fusion Alignment Method (SFA) for the problem of interfering semantics of the model and the problem of a single way of fusion of local inference results. Finally, the SDF-NN natural language processing model is proposed and a multi-view subspace clustering (DLTE) method based on deep low-rank tensor embedding is proposed. The results of the research experiments show that the average performance index of this paper’s word vector model for each task in three corpora ranges from 71.55 to 89.11, and the performance is stable and the time overhead in the three corpora is 3.93, 7.29, and 13.42 minutes, respectively, and the speed of the model has been significantly improved and the overall performance is better. In addition, the natural language processing model (SDF-NN) constructed in this paper achieves the best performance in the comparison test with strong competitiveness, which further validates the performance of the matrix decomposition-based natural language processing model in this paper, and provides the method and direction for its efficient construction in low-dimensional embedding space.