Massive trajectory data pose challenges to management analysis and data mining, and trajectory compression technology has become an effective solution to this problem. Aiming at the problem that most current trajectory compression algorithms need human intervention to set thresholds, this study proposes an adaptive trajectory inflection point extraction and compression algorithm which combines the idea of feature clustering and trajectory partition. Based on the global and local direction characteristics of the trajectory, the algorithm carries out the rough trajectory division, sub-trajectory merging, and fine trajectory division. The experimental results show that with the increasing trajectory size, the proposed algorithm can produce lower direction error and maintain a higher compression rate than other algorithms. The algorithm features adaptive and high-precision inflection point recognition and still has a high reference value under other trajectory compression scenarios.