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Existing deep reinforcement learning algorithms have difficulty converging in environments with large state space dimensions. So a reinforcement learning algorithm based on one-dimensional convolutional recurrent networks that extracts features in the time dimension is proposed. Firstly, a deep reinforcement learning system based on DQN is built. Then a one-dimensional convolutional layer is added into the neural network architecture of DRQN for extracting the features in the time dimension before the LSTM layer. Finally, the new reinforcement learning algorithm is trained and tested in a timing-related environment. The experimental results show that this change can improve the decision-making level of the agent, making deep reinforcement learning algorithms have better performance in non-image input and timing-related environment