Decoding knee motion intention is crucial for the wearable comfort in lower extremity exoskeleton robots. Patients with neurological disorders are often accompanied with lower limb movement disorders assessed by surface electromyography （sEMG） signals. To integrate the motion assessment and joint angle prediction for these patients， a novel CNN-LSTM framework based on the attention mechanism is proposed to predict the knee joint angle for three daily motions， i.e.， horizontal walking， going uphill， and going up stairs， through 10-channel sEMG signals. The prediction error indicators， i.e.， the root mean squared error （RMSE）， the mean absolute error （MAE）， and the coefficient of determination （R²） reach 2.74， 2.50， and 0.97， respectively， outperforming the traditional network. Furthermore， the ablation experiments show the three indicators have decreased by 20.47%， 34.36% and 6.59% on average， respectively. The proposed end-to-end prediction framework based on the attention mechanism can reach the highest prediction accuracy， providing a reference for the human-robot interaction scheme of the lower limb exoskeleton robot system.