Traditional simultaneous localization and mapping (SLAM) algorithms often rely on developers manually setting fixed parameter thresholds based on their experience. However, as the application scenario changes, these parameters often require manual readjustment. To address this limitation, we proposed a method that leverages the deep deterministic policy gradient (DDPG) to adjust visual SLAM parameters adaptively. This method involves selecting visual SLAM parameters to create a continuous action space. We have used the uncertainty of pose to construct a reward function. The agent acts on the visual SLAM environment by selecting the best action based on the output of the DDPG network, and the network parameters are updated according to the environmental feedback. We have proposed a DDPG-based network with improved performance by incorporating convolutional networks and gate recurrent unit. Finally, the experiments on the Euroc MAV and TUM-VI datasets have been conducted. The results demonstrated that the method significantly improved the accuracy of pose trajectory in indoor scenarios while avoiding the cumbersome parameter adjustment process.