<p>Human motion prediction has traditionally concentrated on spatial-temporal modeling and network architecture development, with relatively limited exploration of the transmission and processing of information flow. Incremental learning, which has emerged as a promising direction, aims to reduce noise interference and capture deeper features through finer-grained inputs. The architecture of Graph Convolutional Networks (GCNs) is particularly well-suited for this task, as it effectively captures the spatial-temporal characteristics of human motion, opening new avenues for research. GCNs excel at modeling the complex spatial-temporal relationships inherent in human motion data, making them ideally suited for incorporating advanced learning techniques. In this paper, we introduce a novel spatial-temporal GCN that incorporates incremental learning enhanced by knowledge distillation techniques. This combination allows the model to reformulate motion data, improving feature fusion and effectively reducing noise interference. Additionally, an innovative input smoothing technique is introduced to preprocess motion data, addressing the inherent variability and noise in raw data. Such preprocessing ensures more reliable inputs, thereby enhancing the robustness and accuracy of predictions. Evaluated using the Mean Per Joint Position Error (MPJPE), the model demonstrates superior accuracy compared to state-of-the-art methods.</p>