An experience replay DQN (ERDQN) data transmission scheduling algorithm was proposed for network transmission application scenarios that require low latency and high reliability in the fields of in-vehicle networks, telemedicine, and industrial control. The main purpose and task of this algorithm was to reduce network delay and improve the stability of network transmission. The ERDQN algorithm optimized the queuing strategy at the sender side based on the deadline-aware transport protocol (DTP), and gave fully consideration to the priority of data blocks and Deadline, taking them as important factors for calculating the order of entering the waiting queue, which avoided the problem of losing Deadline of data blocks and reduced the queuing delay of network transmission. Meanwhile, in the congestion control, the current network transmission state was used as the feature vector to predict the parameters of the next network transmission state and give different reward factors for evaluation. Through the iterative learning of the ERDQN network, the optimal parameters were automatically adjusted to fit the current network transmission. The average transmission rate was high and stable during the subsequent network link transmission process, which alleviated the problems of network congestion and transmission stability and reduced the network transmission delay. Experimental results show that the average queuing delay and transmission delay of the ERDQN algorithm were much lower than those of the traditional congestion control algorithm (Reno algorithm), and much higher than the traditional congestion control algorithm in terms of quality of experience (QoE), which could minimize the network transmission rate fluctuation, reduce the packet loss rate, and provide stable and reliable transmission.