Predicting knee joint trajectories from surface electromyography (sEMG) signals holds a significant application value in various fields such as rehabilitation engineering and prosthetics control. However, existing prediction methods often struggle to achieve satisfactory performance due to limited dataset sizes and poor cross-subject generalization capabilities. In this paper, we propose an effective framework that integrates motion decoupling with a conditional diffusion model to address these challenges. Our approach decomposes knee joint angles into shared motion patterns across subjects and individual-specific amplitude parameters, enabling dual-task collaborative modeling that considers both commonalities and individual differences. Furthermore, the conditional diffusion model is employed to generate high-quality synthetic sEMG samples, effectively expanding the available data resources. Experiments conducted on data from 11 subjects demonstrate that our approach achieves a Root Mean Square Error ( \(RMSE\) ) of 3.59 ± 0.88 \(^\circ \) , outperforming the non-decoupled model (4.61 ± 1.58 \(^\circ \) ), the model without diffusion (4.85 ± 1.62 \(^\circ \) ), the Bidirectional Long Short-Term Memory (Bi-LSTM) (6.75 ± 1.33 \(^\circ \) ) and the traditional LSTM baseline (6.88 ± 1.59 \(^\circ \) ).