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The intelligent gas identification system mainly includes hardware circuit, user interface, signal processing and deep neural network model modules. The ESP32-S is used as the main control chip, and the hardware circuit is designed to dynamically modulate the voltage at the heating end of the metal oxide semiconductor gas sensor. The modulated temperature range of the sensor is 150~250 ℃, and the corresponding modulation voltage range is 2.5~ 3.8V in the experiment. The sensor characteristic change curve obtained by thermal modulation is collected at a frequency of 20 Hz and transmitted to the upper computer through the signal transmission function, and the obtained thermal modulation response signal is fed into a self-built convolutional neural network with I-5×5-2×2-200-200-P structure to build a model after signal processing feature extraction. Fast identification detection of target gases is achieved using forward propagation of trained neural network model parameters. Test results in a static gas-sensitive test platform show that the system can effectively achieve the identification function for multiple types of VOC gases, and the recognition accuracy of 42 kinds of test atmospheres of ethanol, acetone and isopropyl alcohol reaches 100%.