NONE 20260602115708025 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON SYSTEMS 1109-2777 2224-2678 Jun Ma School of Music, Dalian Art College, Dalian 116600, CHINA During the present period of cultural integration worldwide, there’s a heredity and innovation conundrum for traditional ethnic vocal arts. The current evaluation system is obviously too subjective and lacks a clear road. There is no clear direction for development. This paper uses an improved BP neural network and LSTM (Long-Short-Term-Memory Network) algorithm to construct a whole evaluation model. To accurately understand the effectiveness and innovativeness of traditional ethnic vocal art inheritance, and to transform the qualitative indicator values into feature vector sets through quantization and to input them into the neural network system so as to achieve an accurate judgment of the status quo of its development and a forecast of the future trends. Collecting multiple ethnic group data and constructing a model database based on traditional multiple school vocal databases. This is compared and analyzed with single-layer BP neural network and classic indicators. From the experimental test results, it can be seen that compared with the single architecture, the proposed integrated architecture has the advantages of faster convergence and higher prediction accuracy. And it also greatly reduces the data bias caused by human factors, which provides scientific basis for the spread and development of ethnic vocal cultural. It also expands the application area of technology for the digital protection of intangible cultural heritage. 06 02 2026 06 02 2026 370 29 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23202.2026.25.29 https://wseas.com/journals/systems/2026/a585102-1060.pdf Liu H, Guo W. Effectiveness of AI-driven vocal art tools in enhancing student performance and creativity. European Journal of Education, Vol.60, No.1, 2025, pp.e70037- e70037. Shen D. Modeling emotional patterns and singing styles of ancient poetry in vocal art songs based on text mining. Applied Mathematics and Nonlinear Sciences, Vol.10, No.1, 2025, pp.1-8. Bin F. The application and inheritance of Chinese ancient poetry and art songs in vocal teaching. Art and Performance Letters, Vol.5, No.4, 2024, pp.1-6. doi: 10.23977/ARTPL.2024.050401. Chen Y. The effective use of traditional music elements in Chinese vocal music art. Research and Commentary on Humanities and Arts, Vol.2, No.8, 2024, pp.66-68 Dai Y. Integration of vocal technique and artistic expression in vocal art performances. Frontiers in Art Research, Vol.6, No.2, 2024, pp.7-11. Zhao X. A study on the inheritance status and enhancement path of Chinese ancient poetry art songs integrated into vocal music teaching in contemporary colleges and universities. Pacific International Journal, Vol.6, No.3, 2023, pp.166-171. Yao L. Research on the inheritance of Chinese traditional vocal art in contemporary opera. Frontiers in Art Research, Vol.4, No.6, 2022 pp.83-86. Bin F . The Application and Inheritance of Chinese Ancient Poetry and Art Songs in Vocal Teaching. Art and Performance Letters, Vol.5, No.4, 2024, pp.1-6. Yao L. Research on the inheritance of Chinese traditional vocal art in contemporary opera. Frontiers in Art Research, Vol.4.0, No.6.0, 2022, pp.83-86. Qi G. Inquiry and Reflection of the Inheritance and Reference of National Vocal Music Art. 4th International Conference on Education, Language, Art and Intercultural Communication (ICELAIC 2017). Atlantis Press, 2017, pp.669-672. Chang W. The integration of artificial intelligence and ethnic music cultural inheritance under deep learning. Computer Science and Information Systems, Vol.22, No.3, 2025, pp.36-36. Gao S, Phanlukthao P, Guo K. Exploring the Preservation, Inheritance, and Digital Development of Ethnic Dance in a Modern Technological Environment. International Journal for Housing Science & Its Applications, Vol. 47, No.1, 2025, pp.181- 187. Zhang Y, Liu C. Melody prediction of vocal performance using LSTM and attention mechanism and its application in folk music innovation. Journal of Computational Methods in Sciences and Engineering, Vol. 25, No.4, 2025, pp.3608-3622. Yu H. Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera. Frontiers in Psychology, Vol. 13, 2022, pp.872433- 872433. Li X, Lin J, Zhang X. Dynamic Transmission and Innovative Transformation of Cultural Heritage: Generative Artificial Intelligence Practices Based on Cultural Cognitive Models. Applied Sciences, 2025, Vol. 15, No.23, 2025, pp.12651.