Simultaneous Localization and Mapping (SLAM) is one of the important research directions in the fields of robotics, autonomous driving, and other fields nowadays. In response to current situation where monocular vision odometry technology is unable to extract sufficient feature points in weak feature environments with few feature points in external environments, a point-line feature fusion algorithm Based on Point-Line Feature fusion Visual Inertial Navigation Systems (BPLF-VINS) is proposed, which takes the mainstream algorithm Monocular Visual Inertial Navigation Systems (VINS-Mono) as the basic framework. An adaptive point-line feature extraction strategy based on different external environments is adopted, which can maximize the role of line features and avoid system resource waste. In addition, the proposed line segment suppression strategy can ensure the quality of line features, which improves positioning accuracy while maintaining system operation efficiency. The improved LSD algorithm has increased efficiency by 50. 07%. In addition, in order to address the issue of long line segments being mistakenly detected as short line segments, an improved line segment merging strategy is proposed to further improve the accuracy of line segment matching. Through a series of testing experiments on authoritative dataset EuRoC, it is shown that the Root Mean Square Error (RMSE) of the improved algorithm is decreased by an average of 16. 03% and 19. 69% respectively compared with that of PL-VINS and VINS-Mono. The experimental results verify the effectiveness of the algorithm.