In order to solve the problem of insufficient exploration ability of the deep deterministic strategy gradient algorithm, a multi-action parallel asynchronous deep deterministic policy gradient(DDPG) algorithm is proposed for the decision-making approach of operational indices in mineral processing based on reinforcement learning. This algorithm uses multiple actor networks for different initialization and training, which greatly increases the exploration to different degrees. The relationship between exploration and utilization is revealed by extending the critical architecture of deterministic selection policy. This algorithm uses multiple DDPGs instead of a single DDPG, which can alleviate the poor performance of one DDPG and improve the learning stability. And it also improves the data utilization efficiency and speeds up the network convergence by using parallel asynchronous structure. Finally, the actor gets better strategy gradient by influencing critic's update. The effectiveness of the proposed approach has been verified by experiment results on decision-making of the operational indices in mineral processing.