This article discusses a method for predicting regional surface subsidence in mining areas using a combination of spatio-temporal correlation, deep learning, and intelligent optimization algorithms. The study area is a mining area in Heze City, China, with a flat terrain and a total area of approximately 259 km. The method aims to achieve high-precision predictions by capturing the nonlinearity of surface subsidence and optimizing parameters.