Aiming at the problem that the performance of lightweight learning-based congestion control algorithms will fall off a cliff in some scenarios， a transmission control protocol congestion control switching scheme based on scenario change was proposed. Firstly， the real-time network environment was simulated by this scheme. Then， the scenario was identified according to the real-time environment parameters. Finally， the current congestion control algorithm was switched to the relatively optimal lightweight learning-based congestion control algorithm in this scenario. Experimental results prove that the proposed scheme is able to significantly improve network performance compared to the original schemes using a single congestion control algorithm， such as congestion control based on measuring Bottleneck Bandwidth and Round-trip propagation time （BBR） and Performance-oriented Congestion Control （PCC） with a total throughput increase of more than 5% and a total delay drop of more than 10%.