Sustainable Development Using Deep Learning and Big Data Analysis: Innovative Development of Music Events and Cultural Tourism

This work aims to achieve innovative development of Xiamen’s music events and cultural tourism through deep learning (DL) and big data analysis (BDA) methods, all while prioritizing sustainable development. In today’s pursuit of green, healthy, and sustainable growth, Xiamen has established new requirements for local music activities and the cultural tourism industry. Specifically, there is a need to adopt innovative approaches that ensure the sustainable growth of these sectors by leveraging the potential of big data. Firstly, data on music activities and cultural tourism from 2018 to 2022 is analyzed in detail. DL structures, such as convolutional neural networks and recurrent neural networks, are used for feature engineering to extract the temporal and spatial characteristics of music activities and cultural tourism data, fully displaying the correlation between music preferences and the regional economy. Relevance judgment and correlation analysis methods are also applied to confirm the relationship between users’ music preferences, Gross Domestic Product (GDP), and per capita disposable income in different regions. Secondly, DL models such as multi-layer perceptron and deep neural networks are employed. These models are trained on large-scale datasets and optimized to simulate the complex relationship between music choices and regional economic indicators. The trained DL model is then utilized for correlation assessment and analysis to explore higher-order relationships within the data, providing profound insights for the sustainable development of music activities and cultural tourism. The results show that the DL-based approach can accurately predict music preferences and reveal the complex and subtle interactions between music choices and regional economies. These findings offer a new approach and perspective for Xiamen to construct an innovative music tourism industry chain and promote the sustainable development of the local economy. The analysis reveals that the diversity of music choices is negatively correlated with GDP and per capita disposable income, while the diversity of user music preferences is positively correlated with both. Therefore, combining the diversity of musical activities with the regional uniqueness of cultural tourism can achieve mu-tual promotion and common development. The DL-based approach accurately predicts us-ers’ music preferences and reveals the subtle interplay between music choices and the regional economy. Furthermore, the innovation of this work lies not only in validating the correlation between musical activities and economic development but also in proposing a new model that tightly integrates music events with cultural tourism development. This provides a scientific basis and practical guidance for constructing an innovative music tourism industry chain in Xiamen. This work introduces novel concepts and methodologies aimed at facilitating the establishment of an innovative music tourism industry chain in Xiamen, thereby fostering local economic development. While this work has made some progress in verifying the feasibility of the method, the specific types of local music fes-tivals that can be developed in Xiamen have not been listed in detail. Future research should explore the specific impact of different music genres on the attractiveness of cultural tourism and how to more effectively integrate local cultural characteristics and musical activities to promote sustained regional economic growth. © 2025, Institute of Information Science. All rights reserved.