This paper analyzes the current teaching development direction of intelligent simulation context, and argues that the development and updating of semi-open human-computer dialogue systems based on scenes and topics need to rely on the division of labor between machines and human beings. In this regard, the development strategy of intelligent teaching of English dialogue interaction is proposed by combining the speech corpus annotation system designed based on artificial intelligence technology and the dialogue interaction teaching strategy derived from the interactive teaching model. The corpus annotation model of multi-layer perceptual machine is designed, which consists of real-time interaction module, core technology and algorithm module and data storage module. Draw a mind map of the framework for analyzing the effectiveness of dialog teaching, and develop a dialog interactive teaching strategy for pre-class dialog, in-class dialog and after-class dialog by invoking the interactive teaching model. Analyze the annotation results of the intelligent annotation model of speech corpus in LDC corpus and UN corpus. Observe and organize the teaching implementation effect of the dialogue interactive teaching strategy, and illustrate the pedagogical feasibility of the English dialogue interactive intelligent teaching development strategy proposed in this paper in combination with the learning achievement and questionnaire results. In the classroom English conversation, the zero feedback of dialog teaching is only 1.60%. The linguistic feedback of dialogue is reflected in the proportion of 33.87% of combing and summarizing, 33.55% of judging the correctness and error, and 30.99% of extending and pursuing questions. The effectiveness of English conversation classroom is improved.

In this paper, we design a cloud-integrated financial robot based on artificial intelligence technology to provide advanced financial analysis and decision support for financial institutions. The robot platform is embedded with a large amount of financial domain knowledge and data, which can provide uninterrupted financial services to customers using a dialog engine. At the same time, it is equipped with the attention mechanism – long and short-term memory neural network model, in investment transactions and credit risk prediction, which can bring a new digital intelligence experience for financial institutions. The standard and similar sentence recognition accuracy of the article robot dialog engine can be stabilized at more than 90%, and the average access time of the user’s access request is about 0.15s. The importance distribution of financial credit risk indicator features is 5~24, and when the number of features takes the value of 10, the risk prediction accuracy of the robot in this article is the highest, 97.98%. When the prediction model is trained to 50~70 epochs, the Loss value of the financial robot converges to 0.15~0.17. The accuracy of the model chosen in this paper for risk prediction as well as stock prediction is 95.35 and 96.2% respectively. And the absolute difference between the predicted stock price and the true value of the model in this paper is 0~0.28 yuan. Combined with DMI strategy for stock trading, the return is 30.7%. The financial robot improves the user experience and increases the value of risk control as well as stock prediction for financial institutions.

In this paper, seven dimensional variables of marketing mode development and product quality improvement are selected under the 5G network security theory. In order to clarify the relationship between the two, the questionnaire was used to obtain the quantitative values of the seven dimensional variables, and then the Pearson correlation analysis was used to test whether the dimensional variables could be used in the regression analysis. After passing the test, multiple linear regression is used to interpret the relationship between the development of agricultural e-commerce marketing mode and product quality improvement. The regression equation of strengthening brand awareness on product quality is B1=0.511+0.439*B4+0.653*B5+ 0.327*B6+0.297*B7, which demonstrates the relationship between product quality enhancement and strengthening brand awareness in the development of agricultural products e-commerce marketing model in a more intuitive way. This study makes people have a more new cognition of the role relationship between the two, which is of great significance for practicing the goal of synergistic development of the two.

Computer multimedia technology has brought unprecedented innovation to the film and television production industry. Multimedia technology in film and television post-production mainly focuses on two aspects of image processing and audio processing, this paper selects the skin color enhancement and voice enhancement for further research. Adaptive skin color enhancement method is proposed, IMCRA-OMLSA audio enhancement method is selected, and relevant experiments are designed to compare this paper’s method with other classical skin color enhancement and voice enhancement methods respectively, and the effectiveness of this paper’s method in skin color enhancement and audio enhancement is examined through the results of subjective and objective evaluation. The accuracy and F1 value of this paper’s adaptive skin color detection method are 0.961 and 0.945, respectively, and the performance of skin color detection is good. The adaptive skin color detection method in this paper has the best performance with a comprehensive evaluation score of 6.81. In the objective evaluation of speech enhancement, the PESQ, STOI, WSS, and RMSE values of IMCRA-OMLSA method in this paper are 2.03, 72.36, and 38.06, respectively, which are all optimal results. On subjective evaluation, the MOS value of IMCRA-OMLSA method is 1.88 which is the highest value.IMCRA-OMLSA method has the best performance for speech enhancement.

In order to improve the intelligent processing capability of the server of the electric power information platform, the intelligent control platform of electric power informatization based on intelligent data analysis is designed. Taking the regional electric power headquarters as the base point, deploying the electric power informatization intelligent management and control workbench, connecting the necessary systems for electric power operation through the telecommunication management network (TMN), and completing the platform hardware structure design. Divide the platform monitoring function into four parts: query instruction issuance, feedback data reception, data parsing, and data storage, and monitor power data in real time. Deploy data collection algorithms on the data collection server to collect power data such as power harmonics, effective voltage and current, active and reactive power, and harmonic distortion. And Deep Belief Network (DBN) is used to train the anomaly detection model, which realizes the detection of abnormal behavior of the system. Determine the experimental methods and steps, and test the results: the server of the intelligent control platform for electric power informatization designed in this project passed the pressure test of the number of clicks per second and throughput of 100 and 500 simulated users, and has superior traffic processing capability. Application test of the platform, through the test to achieve the design requirements of the system’s various functional modules, in the distribution network line and equipment operation status monitoring, fault precision judgment, fault time statistical analysis and daily repair and other work has achieved certain results.

In today’s increasingly complex and dynamic network structure, cloud computing brings great convenience to computer users and meets people’s requirements for rapid computer data processing. The article firstly analyzes the cloud computing architecture and the security threats existing in the cloud environment, and explains the importance of cloud computing access control mechanism to ensure data security, starting from the traditional access control. Then it introduces the multi-authority attribute access control scheme based on blockchain and elliptic curve improvement, on the basis of which it proposes a blockchain-based cloud data security sharing model and a blockchain transaction privacy protection scheme, which both meets the user data privacy protection needs and realizes privacy computing. Finally, the security of the two schemes is analyzed, and compared with other schemes with the same mechanism. The results show that the blockchain-based cloud data security sharing scheme has better performance and scalability, which shows a stable linear growth of 1x, and the time load introduced by this scheme while enhancing the security of the encrypted data sharing system is acceptable compared to the other schemes to satisfy the application scenarios with large-scale access requests. At the same time, the blockchain transaction privacy protection scheme ensures data privacy while the average time consumed meets the user’s requirements for fast response.

In order to improve the accuracy of automatic detection of malicious code, this paper focuses on the “texture” features of malicious code and the characteristics of different types of malicious code, which are also different, and uses them for the automatic detection of unknown malicious code by using the four machine learning algorithms of KNN, RF, NB and SVM to perform single-feature detection and multi-feature (GLCM, LBP and ngram feature merging) detection respectively. Four machine learning algorithms, namely KNN, RF, NB, and SVM, are used to perform single-feature detection and multi-feature (GLCM, LBP, and n-gram feature merging) detection respectively, and analyze the accuracy of the spatial relationship feature-oriented malicious code detection scheme. A multi-version oriented data protection model is proposed for the data storage space, data version, quantity management and recovery requirements involved in service emergency response. The relative performance errors between its data protection scheme and the plaintext scheme and the simple add noise scheme are analyzed. In all four machine learning algorithms, the detection rate of fused features is higher than that of single features, and the maximum difference can reach more than 60%. When takes the value of 9 or 3, the data privacy protection algorithm, the plaintext algorithm, and the noise-only addition algorithm in this paper have similar accuracy rates. With proper noise selection, this paper’s scheme has good performance in real simulation.

As an important part of the excellent traditional culture of the Chinese nation, Chinese Wushu condenses the wisdom of the Chinese nation, contains the genes of Chinese culture, and has important communication value. Based on the big data Hadoop technology, the article proposes a content recommendation design scheme for all-media communication of wushu cultural communication, and introduces the LFM model and MBGD algorithm to construct an intelligent recommendation model of wushu cultural communication content under the framework. Then, based on Lasswell’s 5W model, the fsQCA method was utilized to explore the relevant factors affecting the effect of martial arts cultural communication. When the number of hidden factors of LFM-MBGD intelligent recommendation model is 55, its RMSE is 0.92, and the HR@K value of the model can reach 62.12%. The consistency level of the existence and non-existence states of each conditional variable of the communication effect of wushu culture is less than 0.8, and the overall coverage rate and the coverage rate of each path are higher than 0.85.The wushu culture communication system driven by intelligent technology can start from building an online resource base of wushu culture, broadening the communication paths of wushu culture, sounding the laws and regulations of wushu culture communication, and building the brand of wushu to improve the communication effect of wushu culture.

In this study, the ecological environment landscape pattern index was selected to construct the ecological environment landscape data representation model under the self-organized feature mapping network (SOM) technique. The input data to the model were monitored under unsupervised conditions and made more sensitive to specific characterization information in the neuronal structure (Hebb), which resulted in different groups of regular data. The moving window method shows that the landscape index is unstable under the 1000-3000m window and the magnitude of change begins to decrease at the 4000m scale. The data tends to stabilize at 5000m scale, and the stability of the data decreases at 6000-7000m, and the anomalous data increases at this time. In terms of landscape level, the aggregation and connectivity of the overall landscape of the study area increased and landscape fragmentation, complexity and diversity decreased under the 4000m window. The land use change model based on SOM network can well reflect the law of land use change in the sample area, which greatly expands the spatial analysis research method of land use change.

STEM education emphasizes the in-depth integration between the knowledge of different disciplines, which is based on real problem solving, aims to establish an organic link between education and life, and takes the cultivation of composite talents with a sense of innovation and hands-on ability as the fundamental purpose. Aiming at the current problems of STEM education, the development path of STEM+ education based on digital visual virtual reality is proposed. Then, combining the DEMATEL method and Interpretive Structural Modeling (ISM), the dynamic factors affecting the development of STEM+ education are explored. Finally, the fuzzy set qualitative comparative analysis (fs/QCA) method was used to analyze the group path of STEM+ education high-quality development. The results of the analysis of motivational factors show that the governmental promotion among the extrinsic motivational factors has a high centrality and is a deep factor that drives the development of STEM+ education. Synergistic motivational factors play the largest role among the three dimensions and are the key to ensure the development of STEM+ education. Endogenous motivational factors are the direct motivational factors for the development of STEM+ education and need to be focused on control. The analysis of the grouping paths in region C, for example, shows that there are two high-level grouping paths and three non-high-level grouping paths, multiple grouping paths with different paths, and high-level grouping and non-high-level grouping are in an asymmetric state. There are some differences in the grouping paths in the east, center and west, and the three regions’ high-quality development of STEM+ education cannot be separated from the support of state factors and response factors. This paper provides a path reference for realizing high-level STEM+ education high-quality development.