Parkinson's disease (PD) is currently an ongoing challenge in daily clinical medicine. To reduce diagnosis time and arduousness and even assess PD levels, a temporal pyramid attention-based spatiotemporal (PAST) fusion model for diagnosis of PD is produced by using gait data from ground reaction forces. This model is innovative in two aspects. First, by using the temporal pyramid attention module, multiscale temporal attention is obtained from raw sequences. Second, 1D convolutional neural network and bidirectional long short-term memory layers are used together to learn spatial fusion features from multiple channels in the spatial domain to obtain multichannel, multiscale fusion features. Experiments are performed on the PhysioBank data set, and the results show that the proposed PAST model outperforms other state-of-the-art methods on classification results. This model can assist in the diagnosis and treatment of PD by using gait data.