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As an experimental technique for fluid mechanics, particle image velocimetry (PIV) can extract global and quantitative velocity field from images. With the development of artificial intelligence, designing PIV method based on deep learning is quite promising and worth studying. First, the authors in this paper introduce the optical flow neural network which is proposed in the computer vision community. Second, a dataset including particle images and the ground-truth fluid motions is generated to train the parameters of the networks. This leads to a deep neural network for particle image velocimetry which can provide dense motion estimation (one vector for one pixel) efficiently. The featuring of particle image extracted by the neural network is also investigated in this paper. It is found that feature matching improves the accuracy of estimation. The proposed network model is firstly evaluated by a synthetic image sequence of turbulent flow. A jet flow experiment is also carried out in this paper to validate the practicability. The experimental results indicate that compared with the traditional cross-correlation and optical flow methods, the proposed deep neural network has advantages in accuracy, spatial resolution as well as efficiency. [ABSTRACT FROM AUTHOR]