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There are usually complex environmental conditions such as various equipment and pipelines in the tobacco factory"s tobacco production workshop, making it difficult for intelligent inspection robots to accurately identify and recognize these complex scenes, resulting in a decrease in obstacle avoidance accuracy. In order to improve the obstacle avoidance accuracy of the intelligent inspection robot, a deep learning based autonomous navigation system design for the intelligent inspection robot in the tobacco factory"s tobacco production workshop is proposed. Provide power to the system through the design of the power module, and combine the design of the RF module and baseband signal processing module to complete the hardware design of the inspection robot navigation system. In the software design of the system, according to the characteristics of the corners of the navigation path marking image in the dimensions of visual difference, distance and color, the navigation path marking of the intelligent patrol robot is recognized. By calculating the collision warning distance, the estimated value of the collision probability is obtained. Using the Convolutional neural network model of deep learning, the collision avoidance path of the patrol robot is planned to achieve the autonomous navigation of the intelligent patrol robot. The test results show that the system in the article can successfully bypass obstacles for the inspection robot, improving the obstacle avoidance accuracy to 95.5%, providing a new solution for improving the efficiency and safety of workshop inspection.