Particle image velocimetry (PIV), as a non-contact, global indirect hydrodynamics measurement technique, can capture the velocity field of a fluid from an image to reveal the motion of the fluid. The development of deep learning technology and its use for PIV have significant research value and a potentially wide range of applications. In this paper, the authors propose an improved lightweight convolutional neural network based on the optical flow neural network. The proposed method improves the accuracy of particle image velocity measurement while reducing the parameter quantity of the model and improving the test speed. First, this work improves the optical flow neural network architecture with superior rigid body estimation performance, and uses an artificial particle image dataset for supervised training. The trained network model is then compared with a state-of-the-art PIV deep learning model. Experimental results indicate that the PIV based on the lightweight deep learning model proposed in this paper can reduce the number of model parameters by 9.5% and improve the test speed by 8.9% without losing accuracy.