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A delayed edge based visual inertial SLAM algorithm (DM-VI-SLAM) based on point line feature fusion was proposed to address the issues of low accuracy, perceptual degradation, and poor reliability of single sensor SLAM technology in complex environments, which made it difficult to accurately estimate camera trajectories. Firstly, a factor graph optimization model was employed, proposing a novel structure that taked the inertial measurement unit (IMU) as the primary system and vision as the auxiliary system. This structure introduced auxiliary system observation factors to constrain the biases of the IMU primary system and receiving IMU odometer factors to achieve motion prediction and fusion. Secondly, by adding point and line features in the front-end, a feature matching method based on the midpoint of a line segment was designed. A sliding window mechanism was added in the back-end to achieve historical state information backtracking, and a nonlinear joint optimization problem was constructed to improve matching accuracy. Finally, to accelerate the solution, a delayed marginalization strategy was introduced that allowed for the readvancement of the delay factor graph, thereby generating new and consistent linearization points to update the marginalization. By comparing with typical SLAM algorithms and verifying their effectiveness on EuRoC public datasets and real scenes, experimental results showed that the proposed algorithm had higher accuracy and reliability in complex highspeed motion scenes and low feature texture scenes.