Self-adaptive user interface recommendation method and system based on foot interaction

The invention belongs to the technical field of interface recommendation, and provides a self-adaptive user interface recommendation method and system based on foot interaction, which are characterized in that the sizes of elements in an interactive interface are designed by utilizing the foot length difference of users; using a deep learning field perception decomposition machine method to recommend theme styles and element shapes in the interactive interface; meanwhile, interface libraries with different layouts are constructed by utilizing interface design rules, and element layouts in an interactive interface are recommended through a reinforcement learning multi-arm forcible theft UCB1 algorithm. Through personalized design of elements and layout in the interactive interface, misoperation possibly generated by a user in movement is reduced or avoided, the precision of operation by the user through a natural interaction mode of feet and the interface is improved, the man-machine interaction efficiency is