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On study of surface electromyographic signals (sEMG) pattern recognition in rehabilitation equipment and intelligent prosthetic applications, a square-mediation method that extracts the envelope of multi-channel sEMG features is presented, with which the finger gesture recognition rate and accuracy rate is improved. In the process, the sEMG is squared by the finger movement acquisition experiment, and then the envelope was formed through the low-pass filtering. Using the amplitude-multiplication method, the envelope of different types of finger action is used to creat the teacher sample label. With these label, the BP neural network is used to accomplish the recognition and classification of the action. Experimental results show that the average correct rate of finger behavior recognition is 94.93% , including thumb, index finger, middle finger, ring finger, little finger and all finger flexion actions. The average time delay for each action recognition is 50.7 ms.