To enhance the economy of hybrid power system under SOC balance and power constraints, a hybrid power energy management strategy was proposed based on the preferring reinforcement learning. The strategy treated the energy management problem as a Markov decision process and adopted a deep neural network to learn and build the nonlinear mapping from the input states to the optimal control inputs. Compared with the traditional reinforcement learning algorithm, the preferring reinforcement learning did not require the setting of a reward function and only needed to make preference judgments on multiple actions to achieve the convergence of network training, which overcame the design difficulty of weighting normalization in reward function. The effectiveness and feasibility of the proposed energy management strategy were verified through simulation experiments and hardware in the loop tests. The results show that compared with traditional reinforcement learning energy management strategies, the proposed strategy can improve the economy by 4.6% to 10.6% while maintaining the SOC balance and power constraints of hybrid power vehicle.