Static contact force is an important index to evaluate the performance of pantograph, and also an important factor to measure the contact quality of pantograph, which directly affects the safety of train running. Traditional measurement methods have problems such as low efficiency and poor accuracy. Based on the idea of cloud, tube and terminal of the Internet of Things, this thesis develops a vehicle-mounted pantograph performance detection device, and proposes an adaptive segmental data fitting algorithm to optimize detection accuracy. The algorithm performs data segmentation and curve model optimization at the same time, and realizes adaptive segmentation at the best point by gradually expanding interval length and evaluating five fitting models: polynomial, exponential, Gaussian, Fourier and power function. The experimental results show that the average error rate of the measuring device is reduced from 1.91% to 0.21%, which is better than other matching methods in the thesis and shows a good precision compensation effect.