Aiming at the problem of optimal sensor placement (OSP), a new method based on the deep neural network is proposed, and the optimization of vibration test sensors in a simplified bridge-shaped truss structure is used for verification. First, a traditional sensor optimization arrangement method is employed to find the optimal sensor placement of a large number of automatically generated truss structures. The training set and verification set required by the deep learning method are constructed from the preprocessed optimization results. Second, a deep neural network model adapted to the problem studied in this paper is designed and trained using Python and the deep learning framework TensorFlow. Then, new truss structure parameters are randomly generated. Finally, the optimization results given by the deep neural network and the traditional method are compared, which verifies the feasibility and advantages in speed, portability and scalability of the sensor optimization layout scheme based on the deep learning method.