When the image is blurred due to rapid camera movement or in low-texture scenes， the Simultaneous Localization And Mapping （SLAM） algorithm using only point features is difficult to track and extract enough feature points， resulting in poor positioning accuracy and matching robustness. If it causes false matching， even the system cannot work. To solve the problem， a low-texture monocular SLAM algorithm based on point-line feature fusion was proposed. Firstly， the line features were added to enhance the system stability， and the problem of insufficient extraction of point feature algorithm in low texture scenes was solved. Then， the idea of weighting was introduced for the extraction number selection of point and line features， and the weight of point and line features were allocated reasonably according to the richness of the scene. The proposed algorithm ran in low-texture scenes， so the line features were set as the main features and the point features were set as the auxiliary features. Experimental results on the TUM indoor dataset show that compared with the existing point-line feature algorithms， the proposed algorithm can effectively improve the matching precision of the line features， has the trajectory error reduced by about 9 percentage points， and has the feature extraction time reduced by 30 percentage points. As the result， the added line features play a positive and effective role in low-texture scenes， and improve the overall accuracy and reliability of the data.