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In the process of tobacco processing, the distribution of moisture is influenced by multiple factors such as temperature and humidity, and the control system cannot accurately reflect the overall moisture situation. To comprehensively improve the quality level of processed cigarettes, a tobacco moisture constant control system based on LSTM neural network is designed. Deploy the Profibus control bus and connect the moisture detector and moisture constant device in the circuit system to complete the hardware design of the tobacco moisture constant control system. In terms of system software design, an LSTM neural network unit is constructed, and based on the analysis conditions of tobacco moisture absorption capacity, a specific moisture distribution model is solved to achieve the modeling of tobacco moisture model based on LSTM neural network. Calculate the outlet humidity and outlet temperature of tobacco leaves separately, and combine the transfer function approximation parameters and constant time delay parameters to complete the tuning process of control parameters. Then, combine relevant application components to achieve the design of a tobacco moisture constant control system based on LSTM neural network. The experimental results show that under the action of the LSTM neural network model, the moisture content of raw silk is stably controlled between 13% -18%, and the problem of excessive moisture will not cause the quality level of cigarettes to fail to meet the actual processing standards.