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Aiming at the problem that traditional detection methods are affected by complex environments and human intervention, which leads to low detection accuracy, a CNN deep learning-based robot grabbing position detection method is proposed. According to the basic structure of CNN, the research is based on CNN deep learning detection principle. Divide the robot's grab position template points according to the tangent slope direction, calculate the template matching distance, and get the closest distance from the matching point on the machine template to the edge coordinate image point. Keep the horizontal and vertical coordinate variables unchanged, and observe the distribution of coordinate gray values ??and matching degree functions on the map. Introduce GA to solve the matching method, and find the optimal solution according to the matching process. Analyze the captureable and non-grabbable position information of the color image and depth image, and convert it into a data format conforming to CNN deep learning to complete the information preprocessing. According to the schematic diagram of the robot grabbing operation, a specific detection process is designed, and the detection result is displayed, thereby completing the robot grabbing position detection. From the experimental results, it can be known that the detection accuracy of this method can reach 0.988, and it can be applied to the tasks related to actual robot grabbing.