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Most existing traditional congestion control algorithms are difficult to adapt to the highly dynamic link environment of wireless Ad hoc network. In order to solve the above problem, this paper proposed a method of improving the performance of congestion control based on deep reinforcement learning, Enhanced-CC. It conducted a preliminary detection of the congestion window by using the traditional congestion control algorithm. On this basis, the method used deep reinforcement technology to learn the real-time optimal congestion window range of the link. When the congestion window calculated by the traditional congestion control algorithm was too large or too small, the method adjusted the congestion window, so that the sending rate could match the highly dynamic link bandwidth, and the method could improve the transmission performance of the traditional congestion control algorithm. The experimental results show that Enhanced-CC can significantly improve the performance of traditional congestion control algorithms such as BBR, CUBIC, Westwood, Reno, and is superior to the performance of fully learning based congestion control algorithms such as PCC and PCC Vivace, and the combination of deep reinforcement learning and traditional congestion control algorithms such as Orca and DeepCC. [ABSTRACT FROM AUTHOR]