Aiming at the insufficiency of the classical Douglas-Peucker data compression algorithm, such as low recursion efficiency and uncertain threshold selection, the study proposes an improved feature point extraction method. The algorithm calculates the frequency of data points by histogram, according to the distance from the data point to the baseline and the turning angle between adjacent data points, considers the "isolation" and frequency of data points, and entropy value method is used to obtain the ultimate evaluation value, the curve data is compressed automatically with the given data compression ratio. The simulation experiments are carried out on MATLAB, and using the self-developed control system platform, incremental self-learning for the flow characteristics of inlet metering valve, the corresponding experiments are carried out on pump test bench and diesel engine test rig. Experimental results show that this algorithm can effectively compress data to meet the data compression requirements of measurement system and control system.