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Facing the problem of difficult maneuvering of large machines during field operations and poor fitting accuracy of navigation paths in complex scenarios, a method of extracting navigation paths under the maize canopy was proposed based on deep learning and Gaussian process regression. Firstly, based on the quadruped robot collecting images of crop rows under the corn canopy, the Mask R-CNN instance segmentation method was improved, and the simple path aggregation network (Simple-PAN) was introduced into the feature fusion network, and the bottom-up path augmentation module and the feature fusion operation module were increased to improve the image context feature extraction module and the fusion capability of image context features. Secondly, the dividing line between the two sides of the area was constructed on the basis of the crop row target under the crown identified by the model, the distribution of the drooping leaves on both sides of the passable area was calculated, and the navigation path algorithm was optimized based on weighted average. The Gaussian process regression (GPR) algorithm was improved, and the DotProduct linear kernel was added to optimize the curve fitting and improve the straight line fitting effect of the GPR method. Finally, the navigation path recognition was performed on the validation set, and the average pixel deviation of the navigation paths fitted by different methods was calculated. The experimental results showed that the algorithm was able to adapt to the situation of leaf-obscuring rhizomes in corn fields, the optimized Mask R-CNN model possessed higher target segmentation accuracy under the canopy, the average deviation of the navigation line fitted based on the improved GPR algorithm was 0.7 pixels, and the average time consumed for processing a frame with a resolution of 1280 pixels×720 pixels was 227ms. The algorithm can provide navigation paths with some obstacle avoidance capability under the maize canopy to meet the requirements of real-time and accuracy of navigation. The research results can provide a technical and theoretical support for the research of navigation algorithms for intelligent agricultural equipment in the field.