Optimized integration of traditional folk culture based on DSOM-FCM

Abstract

Traditional folk culture, which records the track of local historical development, is a historical product reflecting the humanistic style and features and has important historical and cultural value. In the context of big data, analyzing the characteristics of traditional folk culture and excavating the internal relationship and implicit information between traditional folk culture data are the common concerns in the field of traditional cultural information science. Based on the research on the digital characteristics of traditional folk art and the data clustering method under the background of big data, this paper proposes a deep self-organizing map fuzzy C-means (DSOM-FCM) model based on dynamic self-organizing neural network, which integrates resources of traditional folk culture in Shaanxi Province. It not only takes into account the needs of economic development and spiritual civilization construction, but also meets the needs of social and cultural development in Shaanxi Province. Firstly, the spatial and temporal characteristics of big data of folk traditional art are analyzed, and then the input vector of dynamic self-organizing neural network is determined to be 6-dimensional attribute data. Then, based on the traditional self-organizing mapping (SOM) algorithm and fuzzy C-means technology, a traditional folk art resource integration model based on DSOM-FCM is constructed. Finally, using the traditional culture data set test model of Shannxi Province, the experimental results are as follows: When SF = 0.35, the number of clusters of the algorithm is 3, and coarse clustering is realized. When SF = 0.7, the number of clusters of the algorithm is 6, and fine clustering is realized. In order to test the efficiency and accuracy of the model, from the perspectives of classification error, iteration time, number of iterations, and number of clusters, a comparison experiment with SOM and TreeGNG algorithms is set; the results show that the algorithm designed and used in this paper performs well in solving the optimization and integration of traditional folk culture.