In this study, the distribution characteristics and changing law of sports comfort perception were analyzed by collecting the comfort evaluation data of running in winter tight sportswear, and proposes a network model based on particle swarm optimization-cuckoo search-long short-term memory to track the changing law of motion comfort. First, considering the existence of redundant features, analytic hierarchy process analysis is used to screen out key features; and then, particle swarm optimization and cuckoo search algorithms are used to optimize the key parameters of the long short-term memory prediction model, so as to avoid the model prediction performance caused by the selection of parameters based on experience. The experiments compared the prediction accuracy of other models, and selected mean absolute error, root mean square error, and mean absolute percentage error evaluation indicators to verify the effectiveness of these models. The results show that the perception of wearing comfort changes over time, but when it reaches the extreme point at a certain moment, and then it gradually falls back. The humidity sense and thermal sense of bust, crotch, and back in human body are the main comfort perceptions that affect movement; LSTM and the optimized LSTM models are suitable for the prediction of comfort perception at different times during exercise. Among them, the PSO-CS-LSTM model can more accurately track the changing trend of motion comfort, the prediction has high prediction accuracy and validity; we selected three different running speeds as the experimental data, which also verifies the universal applicability of the model.