Job shop scheduling problem （JSSP） is a non-deterministic polynomial （NP）-hard classical combinatorial optimization problem. In JSSP， it is usually assumed that the scheduling environment information is known and remains unchanged during the scheduling process. However， the actual scheduling process is often affected by many uncertain factors （such as machine failures and process changes）. A proximal policy optimization with hybrid prioritized experience replay （HPER-PPO） scheduling algorithm is proposed for solving JSSPs with uncertainties. The JSSP is modeled as a Markov decision process where the state features， reward function， action space， and scheduling policy networks are designed. In order to improve the convergence of the proposed deep reinforcement learning model， a new hybrid prioritized experiential replay training method is proposed. The proposed scheduling method is evaluated on standard datasets and datasets generated based on standard datasets. The results show that in static scheduling experiments， the proposed scheduling model achieves more accurate results than existing deep reinforcement learning methods and priority dispatching rules. In dynamic scheduling experiments， the proposed scheduling model can achieve more accurate scheduling results in a reasonable time for JSSP with process order uncertainty.