In order to solve the problem that the actual muscle force is represented by the extremity force and the degree of muscle fatigue is not taken into account in muscle force measurement, this paper studies an upper limb muscle force prediction method based on surface EMG and muscle fatigue. The musculoskeletal model of upper limb was established by AnyBody software, and the muscle force of a single muscle was obtained by simulation of the end force of upper limb. The time of isometric muscle contraction was used to characterize the degree of muscle fatigue. Ten healthy male subjects were subjected to the upper limb isometric contraction experiment, and six eigenvalues of integrated electromyography, root mean square, median frequency, average power frequency, wavelet coefficient and frequency were extracted during the experiment. After analyzing muscle force, eigenvalue and muscle fatigue degree, it is found that the three are highly correlated. The Sparrow search algorithm (SSA) was used to optimize the weights and thresholds of BP neural network, and the upper limb muscle strength prediction model was constructed and trained. The test results show that the error of this method is less than 12%, and it can predict the muscle strength accurately.