AI teaching is an important part of Internet + education and intelligent education, and also an inevitable trend of the times. Through the automatic language and language sense recognition processing technology, the robot can sense the life scene and voice emotion of infants, and automatically recognize and play functional music. Artificial intelligence develops rapidly and is widely used in various fields. Music robot with certain neural network can understand music, analyze music and create music. In the field of professional music education, all kinds of new interactive teaching music intelligent system based on music artificial intelligence technology will be a new mode of perceiving music, cognitive music, creating music and music education.

The development trend of tourism performance networking, although convenient for audience consumption, also makes the performance information present the development trend of big data. In the mass of information, how to accurately locate products and improve audience satisfaction is an urgent problem to be solved. In order to better explore the evaluation of tourism performance by the customer satisfaction evaluation model, analyze the development prospect of tourism in Jiangxi Province in the future, improve the customer satisfaction evaluation model with rough set, and propose a composite customer satisfaction evaluation model. By setting the adjustment value of the evaluation index, the model not only avoids the “false eigenvalue” of the satisfaction evaluation result but also simplifies the calculation process of the model and improves the accuracy, calculation efficiency, and single data processing capacity of the satisfaction evaluation. According to the MATLAB simulation results, the composite customer satisfaction evaluation model constructed in this study is better, the calculation accuracy is >97%, and the calculation time is 40 seconds, which are better than the original customer satisfaction evaluation model. Therefore, the composite customer satisfaction evaluation model can be applied to the evaluation of tourism performance products to provide data support for the evaluation price of audience satisfaction in Jiangxi Province.

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With the rapid development of social economy and people’s increasing requirements for spiritual life, tourism projects are booming. At the same time, in order to improve their competitiveness, tourism regions also pay great attention to the attraction of performing arts projects to the audience, especially the online conversion rate. The original online conversion rate survey method cannot effectively judge the online conversion, and the comprehensive judgment ability is weak. At present, there is a lack of necessary analysis methods in the research of audience online conversion rate, so the research results cannot meet the actual requirements. Moreover, there are some deficiencies in the research depth of conversion rate at home and abroad. In order to shorten the abovementioned gap, comprehensive analysis methods should be applied to focus on online transformation. Based on the abovementioned reasons, this paper proposes a method based on SWOT to build a prediction model of audience online conversion rate. First, SWOT is used to cluster the data of strengths, weaknesses, potential dangers, and competitors and sort the data to judge the importance of different data, so as to improve the accuracy of the online conversion rate judgment results of viewers. Then, SWOT classifies the data to form analysis particles in different aspects and analyzes the co-evolution and optimal results of analysis particles in different aspects. After a simulation test, the SWOT model constructed in this paper is superior to the online conversion rate survey method in terms of calculation accuracy and calculation time and the overall effect is higher. At the same time, the integration of threshold, weight, and other adjustment functions further enhances the analysis effect of SWOT model. Therefore, a SWOT model can accurately and quickly predict the online conversion rate of audience.

The digital music began to spread online from last century 90s. It appears many sites that can audition on the Internet since 20th century, after then digital music gradually spread from the wired network to mobile networks, whose prosperity attracts more and more attention to this field. Digital music has brought new vigor to the gradually depressed music industry, and has brought new hope to the music industry which has been infringed by piracy for a long time. The development of digital music industry, especially the success of mobile music industry, needs us to not only pay attention to the development of the industry but also to accelerate the research on mobile music industry. Taking the development process of mobile music, industry chain and industry environment analysis as the foundation, this paper summarizes the successful business model of the development of mobile music through the case analysis on the successful enterprise in mobile music industry.

Music in the Internet Age has changed in communication, creation, artistic style and aesthetic value orientation, and these changes are the product of the development of the modern era, they are the result of science and technology progress, the social and economic development and people's ideological change. Music communication in the Internet Age not only has overcome the shortcomings in natural transmission time (i.e., the original form of communication), such as narrow communication range and fuzzy communication effect, but also has overcome the limits of both sides isolated, being passive and limited in the traditional communication age, which consolidated and developed the advantages of the two. The most direct embodiment of the music creation's change in the Internet Age is the birth and the popularity of the network songs. Network songs refers to these songs that are created, spread, and become popular through the network. Network song is an entertainment of netizens, the real emotional expression of civilian and a return to the folk music discourse power; and its aesthetic taste trends to interest; network song has its existence rationality, in which the refraction of the social problem is also worth our reflection, and it has positively warning effects on the socialist construction; but there are also negative effects about network songs: the too rebellious God and the contents which are misleading for children and adolescents. It is a problem calls for the concern of the whole society to let the network songs get rid of the two difficult situation.

Under the background of public health events, the government needs to adopt reasonable support strategies to help cultural enterprises tide over the crisis. However, the original analytic hierarchy process cannot be comprehensive and accurate analysis, resulting in that the government support strategy cannot play a role. Fish swarm algorithm is a comprehensive intelligent algorithm, which can comprehensively analyze the factors affecting the formulation of supporting strategies for cultural enterprises, and has the advantages of simple operation and strong analysis ability. Under this background, this paper puts forward a fish swarm model; this method is a comprehensive intelligent analysis method, which can help the government find the best support strategy from the perspectives of politics, economy, and society. In order to verify the effectiveness of the fish school model, this paper uses MATLAB software for verification; the results show that, in the support strategy classification, selection accuracy, and selection time, fish school model is superior to the analytic hierarchy process. Therefore, the fish swarm model can help the government to better choose supporting policies and help cultural enterprises tide over the crisis and improve the accuracy of formulating supporting strategies for cultural enterprises.

The construction of a big data platform is the basis for improving the service level of scenic spots, and it is also a new media way to increase the number of tourists. At present, the scenic spot platform lacks effective evaluation methods and cannot analyze massive data, resulting in an insufficient increase in the number of tourists. Therefore, this paper analyzes the construction of the big data platform from the perspective of sports group performance, aiming at promoting the increase in the number of tourists in scenic spots. Firstly, the continuous clustering sampling method is used to make statistics on the massive tourist data in the platform. Secondly, the equidistant sampling coefficient is added to the sample data to ensure the validity of tourist data.