Aiming at the problem of limb movement limitation caused by severe neuromuscular disease in patients with intact brain cognition, this study tries to enable patients to autonomously control their impaired limbs again and thus proposes an electroencephalogram (EEG) classification method of robotic arm grasping task to carry out rehabilitation training for patients with impaired limb movement. Firstly, the motor imagery EEG signals are collected by a non-invasive EEG technology, and they are then classified and identified by preprocessing, feature extraction, and convolutional neural networks with multi-scale feature fusion. Finally, the label obtained by the classification model is decoded into instructions that can be recognized by the robotic arm, so as to control the arm to fulfill specific tasks. The experimental results show that the EEG data collected from experimentally selected 15 healthy subjects in the motor imagery experiments are feasible, and the average accuracy rate reaches more than 82%, which provides a new idea for EEG classification of robotic arm grasping task.