A Study of Folk Culture in Francophone Central Africa Incorporating Deep Learning

Abstract

Abstract Through the theoretical analysis of folk culture in French-speaking countries in Central Africa, six-dimensional features of folk culture in non-French-speaking countries are summarized, and the folk culture database of French-speaking countries in Central Africa is constructed based on a web crawler. Fusing scene region features with image color and texture features, an Extreme Learning Machine algorithm carries out the classification to realize the construction of a folk culture classification model based on deep learning, and the cross-entropy loss function and Adam optimizer are used respectively to optimize the above-constructed model. The relevant parameters, training set, and test set are determined, and an example analysis of non-Chinese folk culture from the perspective of deep learning is carried out. It is found that the accuracy of non-folk culture classification of the model after Adam and cross-entropy optimization is significantly higher than that of the CNN model, with an average of 0.03~0.04, indicating that after optimization, the model can more comprehensively reflect non-French-speaking countries’ folk culture information and features in images. In addition, there are fewer symbols of non-French-speaking folk culture in the image modality, such as religious beliefs (17, 4.57%) and music and dance (32, 8.60%), which is because it is difficult to represent this type of folk culture with images. This paper fully illustrates the prospects for the application of deep learning models in the study of folk culture in French-speaking Central Africa, which can contribute to the development of digitization and informatization of folk culture in French-speaking Central Africa.