To address the issue that the existing methods for detecting dynamic changes in Leg Length Discrepancy （LLD） under non-clinical environments fail to exploit the spatiotemporal features of LLD gait fully， a novel method based on a graph spatiotemporal learning model was proposed to detect LLD dynamic changes. In the method， the detection of LLD dynamic changes was considered as a gait graph pattern classification problem， and a spatiotemporal gait graph classification model integrating spatial graph convolution and Long Short-Term Memory （LSTM） network was constructed， so that the superior spatiotemporal graph topology learning ability was used to explore significantly different features hidden in LLD gait patterns， which improved generalization ability of the gait classification model effectively and detect LLD dynamic changes accurately. Skeletal data simulating different degrees of LLD were collected from 26 healthy subjects using Azure Kinect to build LLD gait patterns， which were used to evaluate the effectiveness of the proposed method. Experimental results show that the constructed model can explore significantly different features hidden in LLD gait graph patterns effectively with a classification accuracy of 99.62%， and its classification generalization performance is improved by 5.83 percentage points compared to that of the model combining Convolutional Neural Network （CNN） with LSTM， providing a novel technical solution for detecting LLD dynamic changes in outdoor environments accurately.