The digital protection and development of folk dance art play a crucial role in bridging the gap between science and the humanities, enabling the integration of science, technology, and the humanities. The article initially employs an optical motion capture system to gather data on the movements of folk dance art. Subsequently, it merges this data to create a digital archive of folk dance art and explores the specific applications of this digital archive. To eliminate abnormal data from the motion capture process, this paper introduces the Kalman filtering algorithm, which preprocesses the folk dance action data. The PAFs algorithm then extracts the key points of the human skeleton from the folk dance action. Then, the coordinates of folk dance action features are changed by Euler angles to extract the corresponding action features and combined with the feature vectors of the action sequence to realize the gesture matching of folk dance actions. The KANO model and regression analysis exploration are the primary means of data analysis for the development of folk dance art inheritance. Using the PAF algorithm to extract the human skeleton key points of folk dance art, the average precision rate is 82.52%, the convenience of the audience’s demand for the digital experience of folk dance art has the highest better value of 0.712, and the regression coefficient of closeness to life is the largest at 0.283. Folk culture should inform the digital preservation and inheritance of folk dance art, expanding its dissemination and fostering its digital development.