Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR), which can maintain maximum transmission rate and minimum latency in a network link. However, the BBR algorithm was reported to cause serious round trip time (RTT) fairness problems by some evaluation experiments. The impact of the mismatch between pacing rate and bottleneck bandwidth caused by the asynchronous detection mechanism of BBR algorithm was analyzed to optimize the RTT fairness, and an optimized algorithm BBR-adaptive (BBR-A) was proposed based on pacing gain model. According to the relationship between RTT and pacing gain, a pacing gain adjustment model based on inverse proportional function was established, which replaces the fixed pacing gain coefficient in the original BBR algorithm. By interleaving the up and down pacing gain coefficients to adjust the pacing rate, each BBR flow could compete for bandwidth resources fairly. Experimental results of network simulator 3 (NS3) show that the channel utilization of BBR-A algorithm was slightly improved compared with BBR algorithm. In the experiment of RTT fairness, BBR-A reduced the throughput difference between different RTT flows, and Jain fairness index was at least 1.5 times higher than BBR algorithm with different buffer sizes and RTT differences. The retransmission rate of BBR-A algorithm was significantly reduced. By adaptively adjusting the pacing gain coefficient, the pacing rate between different flows was balanced, and the RTT fairness of BBR algorithm was improved.