Adaptive Clustering Algorithm for Teaching Resources of Chinese Flower and Bird Painting Practice and Theory Course

Abstract

Painting is a two-dimensional visual language that expresses ideas and emotions while also creating aesthetic aspects. Flower and bird painting embodies an aesthetic ideal. For Chinese people, it serves as a harmonious coexistence with natural objects. Based on the contemporary context and the concept of environmental aesthetics, flower and bird painting should go beyond the sensual portrayal of plants and animals, individualistic lyricism, and the tendency of commercialization. It helps to examine the important value of plants and animals in the spiritual life of human beings from the perspective of the community of human destiny. It also raises the concern of contemporary people for the natural environment and the survival status of natural objects. Free from one’s sorrow and happiness, the painting focuses on the pressing ecological issues of universal concern to all mankind and becomes art with world significance expressed in Chinese aesthetic symbols and artistic forms. It is important to classify the teaching resources of Chinese flower and bird painting theory and time. The traditional Chinese flower and bird painting theory and practice course teaching resources classification method are inefficient; for this reason, the adaptive clustering algorithm of teaching resources of Chinese flower and bird painting theory and practice is proposed. Using mutual information technology to obtain incremental data, the digital Chinese flower and bird painting theory and practice course teaching resources features are extracted. It features a tree to be constructed according to the two-step clustering algorithm. The leaf nodes of the feature tree are grouped according to the hierarchical coalescence algorithm. The Euclidean square distance is used to obtain the Chinese flower and bird painting theory and practice course teaching resources square root measure continuous variables. The number of clusters with the maximum growth distance represents the final number of clusters. The results show that the method can realize the effective grouping and classification of Chinese flower and bird painting theory and practice course teaching resources.