The selection of MOOC teaching resources is influenced by diversified resource positioning methods, which leads to low index efficiency of resource mining. Therefore, this paper proposes a multiresource mining method based on association rules to collect the learning behavior data of MOOC users and establish the MOOC teaching resource warehouse. Aiming at the attribute set of information association positioning, the association rules of teaching resources are designed. In addition, the association rules are combined with the shortest path scheduling scheme of teaching resources to establish the location and mining of diversified MOOC teaching-associated resources. Finally, the clustering method is used to process the results of teaching resource mining and complete the clustering of diversified teaching resources. Experimental results show that the index time required by the proposed mining method is 0.1 s, which is only 1/6 of other resource mining methods.
The existing fuzzy assistant cognition system has the problem of imperfect retrieval function, which leads to long execution time. This paper designs a fuzzy assistant cognition system of international economic and trade information under supply chain management. Hardware Part. Optimize wireless sensor and connect power interface and related accessories. In the software part, the dynamic characteristics of international economic and trade information elements are extracted, the flexible operation model of supply chain management is constructed, the demand-oriented organizational structure is established, the output set is determined by the product reasoning, and the retrieval function of fuzzy assistant cognitive system is designed by using the fuzzy association algorithm. Experimental Results. The average execution time of the fuzzy assistant cognitive system and the other two systems is 123.593 s, 165.733 s, and 169.506 s, which proves that the cognitive system integrated with supply chain management has a higher practical application value.
Not only are numerous components absorbed and modified in terms of movie subjects, but also various art forms like painting and music are researched and examined in the production of Japanese cinema, resulting in the development of film styles with national characteristics. The fundamental role of film industrialization is to increase production efficiency, reduce industry risks, create job opportunities, and provide related technologies and services for professional creative people, so that the creativity crystallization of all industry insiders can become an invisible creative power and tangible economic values in the economy and can circulate continuously within the overall film industry structure. On the basis of summering and analyzing of previous research works, this article expounded the research status and significance of Japanese film industry, elaborated the development background, current status, and future challenges of film industry model, introduced the creation and production of genre films in Japanese film industry, established the relationship between personalized creation and market trend of Japanese films, proposed the competition and cooperation model for the distribution of local and imported films, analyzed the market share of sequel and anime films in Japanese film industry, conducted the analysis of terminal construction changes and localized cinema lines operation, and discussed the exploration and management model of film screening bodies. The research results of this article provide a reference for further researches on the development model of Japanese film industry.
Artificial intelligence companies are different from traditional labor-intensive and capital-intensive companies in that their core competitiveness lies in technology, knowledge, and manpower. Enterprises show the characteristics of a high proportion of intangible assets, strong profitability, and rapid growth. At the same time, there are also the characteristics of high risk and high uncertainty. In addition to the existing value brought by existing profitability, corporate value should also consider the potential value brought by potential profitability. Enterprise value is affected by many factors such as profitability, growth ability, innovation ability, and external environment. Traditional valuation techniques are often utilised to value artificial intelligence businesses in the present market. Traditional valuation methods ignore the dynamics and uncertainties of artificial intelligence enterprise value evaluation, make static and single predictions of future earnings, ignore the value of enterprise management flexibility, and are unable to assess the intrinsic value of artificial intelligence businesses. Based on the projection pursuit method, this paper constructs a modern high-quality development enterprise high-quality development evaluation model, uses real-code accelerated genetic algorithm to optimize the projection objective function, and calculates the best projection direction vector and projection value. The collected sample data can be imported into the evaluation model to calculate the comprehensive evaluation value of the high-quality development of modern high-quality development enterprises and the weights of various indicators included. By comparing the size of the comprehensive evaluation value, each sample can be calculated Evaluation of the level of high-quality development. The results show that the high-quality development level of China’s overall economy is on the rise, but the level of development is still low, and there is a large gap between the development level of the eastern region and the central and western regions. Using the systematic generalized moment estimation method, empirically, we analyse the impact of artificial intelligence on the high-quality economic development. The results show that artificial intelligence at the national level and in the central and western regions will significantly promote high-quality economic development, while artificial intelligence in the eastern region has a significant inhibitory effect on high-quality economic development.
Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
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