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In response to the problems of low recognition rate and unstable response in traditional gesture recognition systems, a flexible strain sensor data glove gesture recognition system was developed, which included flexible sensors, signal acquisition systems, and gesture recognition algorithms. The system can accurately capture the motion information of each finger joint, and had the characteristics of high degree of freedom, low cost and high recognition rate. Carbon black (CB) and carbon nanotubes (CNTs) were doped into soft silica gel, and a resistive sensor with good linearity and high sensitivity was designed by extension technology. The experimental results showed that the sensor had good static and dynamic response characteristics, and the sensor calibration was completed. Using multiple flexible sensors to prepare data gloves and build a signal acquisition system, a gesture recognition method combining BP neural network and template matching technology was further proposed to improve the recognition rate of similar gestures, and the recognition rate of the algorithm was 98.5%. Gesture recognition experiments were carried out for different groups of people. The results showed that the accuracy of the gesture recognition system reached 92.8%, and the response time was about 40ms. The data glove had good application potential.