As a carrier of culture, folk music is the embodiment of social economy, nature and culture, and reflects the social, historical and aesthetic conditions at that time. This can understand history, aesthetics, values and so on from another perspective. Folk music has a certain role in promoting people's self-cultivation. Ethnic music is the carrier of a country's beliefs and values. Its prosperity promotes national self-confidence and enhances the country's patriotism. However, in the era of big data, due to the proliferation of popular music, people tend to ignore the importance of ethnic music, including some textbooks and courses, and even ignore ethnic music directly. Therefore, this paper mainly studies the aesthetic education function of digital information resources in the teaching of ethnic vocal music in colleges and universities under big data. This paper proposes how to solve this situation, and conducts market research and analysis. The interviews and questionnaires were conducted on all the first-year students of a middle school in Xiamen and the high school music teachers, and the clustering algorithm was used for data analysis, which also made the experimental results more accurate. The test results show that in the case of dissatisfaction with the teaching materials, students believe that the proportion of popular music should be increased, and the proportion of film and television animation music is as high as 99% and 89%. The proportion of choosing to increase Chinese folk music and opera music is pitifully small, only 2% and 1%. This fully shows that the teaching of ethnic music appreciation in colleges and universities has not achieved the desired effect at all, and the inheritance of ethnic music is in a dangerous situation. Therefore, after the research of this experiment, it is also necessary to call on everyone to protect Chinese national vocal music.

Jiani Wu. The Aesthetic Education Function of Digital Information Resources in the Teaching of Ethnic Vocal Music in Colleges and Universities Based on Big Data. International Journal of Educational Curriculum Management and Research (2023), Vol. 4, Issue 3: 66-82. https://doi.org/10.38007/IJECMR.2023.040308.

[1] Kang H . Analysis on selection of vocal music works and effective teaching methods in vocal music teaching in colleges and universities. Revista de la Facultad de Ingenieria, 2017, 32(15):280-283.

[2] Fu L . Discussion on the Differences between Theory and Practice in Vocal Music Teaching. Region - Educational Research and Reviews, 2021, 3(1):6-9.

[3] Rosenzweig S , Scherbaum F , Shugliashvili D. Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology. Transactions of the International Society for Music Information Retrieval, 2020, 3(1):31-41.

[4] Jinxu H . Study on the relationship between Chinese opera and opera music record. Agro Food Industry Hi Tech, 2017, 28(1):858-861.

[5] Xue J W , Xu X K , Zhang F . Big data dynamic compressive sensing system architecture and optimization algorithm for internet of things. Discrete and Continuous Dynamical Systems - Series S, 2017, 8(6):1401-1414.

[6] Kuang L, Hao, Yang L T. A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction. IEEE Transactions on Emerging Topics in Computing, 2017, 2(3):280-291.

[7] Stergiou C , Psannis K E . Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: a survey. International Journal of Network Management, 2017, 27(3):1-12.

[8] Savage H D . Reimagining Fauré's Solo Vocal Music: New Editions and Recordings of the Songs and Vocalises. Nineteenth-Century Music Review, 2017, 14(2):1-16.

[9] Yakhno O . Vocal stylistics in rock music: dialectics of general and special. Aspects of Historical Musicology, 2020, 21(21):279-292.

[10]Romania. Critical reception of late 19thcentury Iai-based music. Alexandru Flechtenmacher. Artes. Journal of Musicology, 2018, 18(1):190-206.

[11]Yaoxue, Zhang, Ju. A Survey on Emerging Computing Paradigms for Big Data. Chinese Journal of Electronics, 2017, 26(1):1-12.

[12] Kusiak A . Smart manufacturing must embrace big data. Nature, 2017, 544(7648):23-25.

[13] Xu L , Jiang C , Wang J . Information Security in Big Data: Privacy and Data Mining. IEEE Access, 2017, 2(2):1149-1176.

[14] Zhang Y , Qiu M , Tsai C W. Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data. IEEE Systems Journal, 2017, 11(1):88-95.

[15] Rathore M , Paul A , hmad AA. Real-Time Big Data Analytical Architecture for Remote Sensing Application. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 8(10):4610-4621.

[16] Xing H , Qian A , Qiu R C. A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE Transactions on Smart Grid, 2017, 8(2):674-686.

[17] Wang Y , Kung L A , Byrd T A . Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting & Social Change, 2018, 126(JAN.):3-13.

[18] Calude C S , Longo G . The Deluge of Spurious Correlations in Big Data. Foundations of Science, 2017, 22(3):595-612.

[19] Zhou L ,  Pan S ,  Wang J. Machine Learning on Big Data: Opportunities and Challenges. Neurocomputing, 2017, 237(MAY10):350-361.

[20] Cai H , Xu B , Jiang L. IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges. IEEE Internet of Things Journal, 2017, 4(1):75-87.