The invention belongs to the technical field of artificial intelligence, and particularly relates to a network autonomous intelligent management and control method based on deep reinforcement learning. The method comprises the steps of firstly constructing a network topology, then introducing a CNN, an LSTM layer and a delay updating strategy to construct a routing decision-making model based on a DDPG reinforcement learning algorithm, and finally performing iterative training on the routing decision-making model based on deep reinforcement learning. In each iterative training, an intelligent agent obtains an output action, namely a group of link weights, according to a measured network state and a neural network, and calculates a service route by using a shortest path algorithm according to the link weights. According to a routing calculation result, the intelligent agent issues a flow table, and acquires end-to-end time delay and a packet loss probability of the service to calculate a reward value of the iteration. The algorithm has good convergence, and can effectively reduce the end-to-end delay and packet loss rate of the service.