Most mainstream visual-inertial SLAM systems rely on the point feature method of front-end images for motion estimation and localization. However, the SLAM system based on the point feature method, in the case of weakly textured environments, may not be able to estimate the motion due to the lack of point features. For this reason, the introduction of line features has received extensive attention. Line features in the environment can improve the localization accuracy of SLAM, but the extraction and matching of line features are relatively time-consuming and cannot satisfy real-time requirements. Based on the EDLines algorithm, we propose a fast visual-inertial odometry that fuses point and line features. The improved EDLines algorithm is used to process line features. By adjusting the internal parameters of the algorithm, the extraction time of line features is decreased while the extraction effect is the same. And the line features screening strategy is proposed to filter out invalid short line segments and merge adjacent line segments. We use the more effective line features after screening for graph matching and back-end optimization, which reduces the calculation amount of line features matching and ensures the accuracy. The proposed algorithm has been tested on the public datasets, and the comparison results with PL-VIO and VINS-Mono show that the algorithm in this paper improved real-time performance and localization accuracy.