The processing of device tasks in the industrial Internet requires a large amount of computing resources, and the tasks with low latency requirements have increased significantly. Edge computing places computing power and other resources on the side close to the demand to provide effective support for task processing. However, due to the limited edge computing resources, the requirements of low latency and high completion rate of the device tasks cannot be satisfied at the same time. It is still a great challenge to determine a reasonable offloading decision and task scheduling. Given the above problem, a deep learning-based dynamic priority task scheduling algorithm DPTSA is proposed in this study. Firstly, the tasks to be processed are selected according to dynamic priority and task scheduling decisions are generated through neural networks. Then, a set of feasible solutions are generated through cross-variance and other operations, and the optimal solutions are screened out and stored in the empirical buffer area. Finally, the neural network parameters are optimized through the empirical buffer samples. The experimental results based on Google’s Brog task scheduling dataset show that DPTSA is superior to the four benchmark algorithms in terms of task waiting time and task completion rate.