Digital teaching strategies can significantly stimulate students’ interest in learning and provide personalized learning pathways. This paper proposes a multimodal action recognition method that integrates the word vector method, and designs a teaching decision optimization strategy based on this idea. Firstly, we compare the information of different modalities, complete the construction of multimodal action recognition network through the processing of image information and optical flow information, and combine the word vector method to guide the semantic learning of students’ actions. Then the design and realization process of the teaching decision aid system is introduced. Based on the above proposed action recognition method to collect students’ classroom behavior data for model training to be used in the system, the system consists of four modules: model training, classroom data collection, behavior recognition and data presentation. After the data collection, the action recognition of student behavior is carried out to provide teachers with feedback on student behavior information and assist them in making teaching decisions. In this paper, the above algorithms and systems have been verified by relevant experiments. After comparison with other algorithms, it is verified that the multimodal action recognition method designed in this paper, which incorporates the word vector method, has a high accuracy rate. In the comparison of the overall quality of instructional design decisions, the average value of the instructional decision aid system in this paper is 17.35, which is higher than the average score of excellent human teachers in the overall quality of instructional design decisions, indicating that the instructional decision aid system designed in this paper achieves the optimization of instructional decisions and reaches the level of excellent decisions.

This paper proposes a user electricity data mining method based on deep learning and improved locust optimization algorithm, and at the same time adopts the Pearson correlation coefficient method to reduce its dimension to improve the data mining effect of linear weighted KFCM algorithm. In order to deal with the electricity demand of massive electricity customers, the user electricity demand forecasting model is constructed based on the Extreme Learning Machine ELM algorithm by combining the relationship between short-term loads and factors of electricity customers. Construct the service optimization model with the maximization of benefit index as the objective function, and use the BAS algorithm to solve the optimal solution in order to achieve the effect of user service optimization. Determine the experimental platform and model parameters, and carry out an example analysis of demand forecasting and service optimization for electricity users.C class users have a small electricity load except for breakfast and dinner, and the maximum time period of the electricity load is from 18:00 to 20:00 hours. Combined with MAPE, the ELM model improves 4.57% than SVR, 21.9% than LSTM, and 34.37% than ARIMA, which indicates that the ELM model is more effective and higher in demand forecasting for electricity users. In addition, the optimal solution of the effect of the BAS algorithm is 69 yuan, 102 yuan and 49 yuan higher than that of the GA algorithm in terms of dividend transmission benefit, energy saving and emission reduction benefit, and electricity right trading benefit, respectively, and the optimal solution based on the BAS algorithm is closer to the actual benefit value, which fully proves the effectiveness of the service optimization model based on the BAS algorithm.

Smart contract technology based on artificial intelligence background is gradually becoming a brand-new path to improve the efficiency of economic transactions due to its unique advantages. This paper initially explores the impact of smart contract technology on economic transaction efficiency through empirical analysis of models and data. The credit mechanism is introduced as an intermediate variable to analyze its mediating effect in the process of improving economic transaction efficiency by smart contract technology. The optimization of Fabric transaction mechanism is realized by using the improved credit model, which further exerts the role of smart contract technology in enhancing economic transaction efficiency. The principal component analysis is used to calculate the comprehensive score of economic transaction efficiency before and after the optimization of smart contract trading mechanism to show the effect of the development of smart contract technology on the improvement of economic transaction efficiency. This paper concludes that the development of smart contract technology will significantly and positively promote the improvement of economic transaction efficiency through benchmark regression analysis, mediation effect test and other methods. After the optimization of smart contract transaction mechanism, the comprehensive score of economic transaction efficiency produces significant improvement compared with the pre-optimization period, in which the average value of the comprehensive score of transaction efficiency in Guangdong, Jiangsu, Shanghai, and Beijing is improved by 20.18%, 24.52%, 33.77%, and 35.54%, respectively. It further indicates that smart contract technology is an effective path to improve economic transaction efficiency.

In the context of globalization where market competition is becoming more and more intense, strategic human resource management (SHRM) plays an important role in enhancing corporate competitiveness. Through structural equation modeling and multiple linear regression methods, this paper reveals the complex paths of SHRM perceptions on employee proactive behaviors, and uses convolutional neural network pairs to explore the nonlinear relationships in the model and validate the results of SEM analysis. Overall, SHRM perception has a significant positive effect on employee proactive behavior (β=0.254, p<0.001). The indirect effect values of job self-efficacy and conceptual psychological contract as mediators were 0.1043 and 0.1726, respectively, which were both positive, i.e., they played a mediating role in the mechanistic effect of SHRM perceptions on employees' proactive behaviors. Insider identity perception, on the other hand, has a significant positive moderating effect between SHRM perception and employee-initiated behavior (β=0.09, p<0.01). The importance of independent variables using CNN model was ranked in descending order as: conceptual psychological contract, job self-efficacy, SHRM perception, job category, and insider identity perception, which was consistent with the results of SEM analysis, revealing the significance of convolutional neural network in optimizing human resource management strategies and enhancing employee motivation.

The rapid development of information technology makes intelligent decision support system play an increasingly important role in economic standardized management. The Intelligent Decision Support System (IDSS) constructed in this paper includes interaction layer, analysis layer and data layer. The system standardizes the management of enterprise economy through strategic forecasting and decision analysis, economic planning and control, and economic analysis. The study combines the fuzzy hierarchical analysis method (FAHP) and the fuzzy comprehensive evaluation method (FCE) to evaluate the standardized level of economic management of enterprise A. The evaluation score of the standardized level of enterprise A’s economic management is , which is greater than 80, and it belongs to the grade of “good”. It shows that the intelligent decision support system constructed based on this paper can effectively help standardize the management of enterprise economy.

With the development of renewable energy technology and the pursuit of sustainable development in the construction industry, the design of direct-soft photovoltaic systems integrated with buildings has become an important research direction. In this paper, a variety of photovoltaic power generation modules are selected and combined with building roof functions to design a solar photovoltaic building integration system. In addition, this paper constructs a multi-objective optimization configuration model, improves the multi-objective particle swarm algorithm, and analyzes the optimization effect of the improved particle swarm algorithm on the photovoltaic building integration system by using multiple sets of test functions and evaluation indexes, combined with a number of experiments. The improved particle swarm algorithm in this paper converges to the optimal value of 0.21 when iterating to 25 rounds. And with the increase of the number of nodes, the optimized particle swarm algorithm, the distribution of node voltages in the vicinity of the standard voltage. The PV building integrated system designed in this paper still has a generation output efficiency higher than 85% after 20 years, which shows good stability of power generation. And the power generation in its whole life cycle is about 1645710kwh, which greatly reduces the consumption of conventional energy. In conclusion, the PV building integrated system in this paper not only has significant advantages in terms of capacity efficiency, but also shows strong potential for environmental protection.

As the birthplace of national culture, traditional villages can convey cultural and social natures through spatial configuration. Based on the theory of spatial syntax, this paper combines the genetic algorithm to design the fitness function for optimization, and selects the streets and lanes of Wengji Village as the research sample, focusing on the analysis of its morphological evolution mechanism from 1975 to 2020. Through quantitative analysis, it is found that although the streets and alleys of Wengji Village show spatial scale expansion due to social and economic development, the village streets and alleys can still maintain the original spatial texture and style. The integration degree, selectability, synergy (0.4273~0.6395) and comprehensibility (0.3744~0.5761) of the streets and alleys in Wengji Village are all characterized by increasing, indicating that the spatial accessibility, spatial openness and spatial wholeness of the streets and alleys in Wengji Village have been improved. However, the degree of synergy and comprehensibility are still lower than 0.7, and there is some room for optimization of the wholeness and cognizability of the streets and lanes of Wengji Village. It is necessary to protect and continue the overall structure of the village, optimize and integrate the key spaces of the village, and rationally control the development process of the village, so as to promote the protection of the spatial form of the streets and lanes of Wengji Village and the continuation of the cultural lineage.

In the context of big data, many problems connected with usage of classical techniques in computing large and complicated data have emerged. For a true disruption of silicon and computational power, there is quantum computing which is poised to redefine itself in the future. Quantum computing is the area of study for this research; real-time big data processing with a specific emphasis on speed. In a sequence of experiments, the performance of quantum algorithms was compared to the performance of classical algorithms regarding large-scale data processes. These findings imply that the compared quantum algorithm has enhanced the processing time while providing on average 70%-time reduction as well as 30 percent improvement of the computational efficiency. Besides, the scalability of quantum computing is also better; it remains effective when used on large datasets, and it has an accuracy of 95% that is higher than classical one. This outcome depiction the power of quantum computing in the alteration of data processing tactics. However, there are some issues like quantum decoherence and error rates, which are guaranteeing the non-stability of quantum computing, but they are expecting more improvements in the field of quantum hardware and error correction. From this study, it consequently becomes clear why the advancement of quantum technology should continue and for what; to meet the challenges of big data.

The promotion of industrial digital transformation is an important breakthrough in the change of economic structure and physical space layout, which can promote the entire industrial chain to the high-end value chain and win more profit space and voice for the integration of domestic and international industries into the international cycle. This study takes the cities in the Yangtze River Delta Economic Belt as an example to deeply explore the spatial effect of digital transformation on the healthy transformation of traditional industrial structure, and constructs relevant spatial coupling models to carry out empirical verification by taking the opportunity of putting forward relevant assumptions. The experimental results show that the model is significant at a significance level of more than 5%, which is suitable for the selection of spatial measurement model. The mean square error of its network simulation output is 0.1333, which verifies the expected hypothesis and proves that the digital transformation of the model has a significant spatial driving effect on industrial upgrading.

In recent years, ideological and political theory (IPE) education has received more and more attention. System theory can well construct the IPE teaching mode, so we need to have a good grasp of system theory. This paper starts with the significance of system theory in the practical teaching of IPE, finds the IPE curriculum that conforms to system theory, and constructs the basic mode of practical teaching of IPE on this basis. Using the idea of a multi-level fuzzy comprehensive evaluation to quantify teachers’ teaching quality, this paper establishes an algorithm model to quantitatively measure teachers’ teaching quality fuzzy.