In order to achieve an intelligent prosthetic hand that can naturally simulate the continuous motion of a human hand, this paper proposes a DF-ANN model based on sEMG to estimate the finger joint angle. The method introduces the SE-Net module in the channel attention mechanism to enhance the relevant feature expression of sEMG, reduce the loss of essential features of sEMG, and effectively improve the performance of the regression model. 10 healthy subjects were selected for experiments with 10 different hand gestures, and regression measures such as R-Squared (R 2 ) were chosen to evaluate the accuracy of the method’s joint angle estimation. The experimental results showed an R 2 of 86. 5%. Compared with the DF-ANN model without introducing SE-Net, the deep forest, and an artificial neural network alone, the R 2 is improved by about 4%. It indicates that the method effectively reduces the error of successive decoding of joint angles of sEMG and can contribute to the supple control of intelligent prosthetic hands.