Convolutional neural network (CNN)-based models are widely used in human movement decoding based on surface electromyography. However, they capture only the spatial information of the surface electromyography and lack prior knowledge of the system, resulting in unsatisfactory decoding accuracy. To address these issues, we propose an attention-based Kalman filter scheme (AKFS), which uses an attention-based CNN model to better extract temporal information and a KF to add prior knowledge of the system. We further solve the problem of insufficient data due to the short training time of new subjects by using a transfer learning method based on a fine-tuning strategy. The proposed scheme was tested in four scenarios: intra-session, intra-session long-time use, inter-subject, and inter-subject with a fine-tuning strategy. The proposed attention-based CNN model outperformed the vanilla CNN model and a hybrid CNN-long short-term memory (LSTM) model in intra-session and intra-session long-time use. After fine-tuning with a small amount of data on a new subject, the attention-based CNN model achieved higher decoding accuracy than the vanilla CNN model and lower response time than CNN-LSTM model. Furthermore, the schemes with KF outperformed the schemes without KF in all scenarios. Our proposed scheme improves the decoding accuracy of the traditional CNN model for a single subject by better capturing the temporal information of the surface electromyography signal and increasing the prior knowledge of the system. Additionally, the proposed scheme can be easily transferred to a new subject using only a small amount of data.