Aiming at limitations of traditional Photovoltaic Hosting Capacity （PHC） assessment methods， such as over-reliance on detailed physical models and high computational complexity， as well as challenge of improving accuracy and stability of deep reinforcement learning methods in policy optimization， a model-free PHC assessment method combining attention mechanism and deep reinforcement learning was proposed. In the method， a Deep Neural Network （DNN） was constructed to predict node voltages and was combined with Soft Actor-Critic （SAC） algorithm for PHC assessment， and intrinsic correlation between state-action pairs was focused on by the interactive attention mechanism adaptively， so as to improve Q-value estimation accuracy and training stability. Case study was conducted in a real power distribution network and the proposed method was compared with the DIgSILENT-based physical modeling method and the model-free method without attention mechanism. Experimental results demonstrate that the proposed method achieves Normalized Mean Absolute Error （NMAE） of 0.045 2， Normalized Root Mean Square Error （NRMSE） of 0.061 8， Mean Absolute Percentage Error （MAPE） of 5.93%， and coefficient of determination （R²） of 0.930 5， and the maximum voltage deviation of ±3 V， verifying the validity of this method.