To address the problem that traditional PhotoVoltaic （PV） power prediction models are affected by random power fluctuation and tend to ignore important information， resulting in low prediction accuracy， ADDPG and ARDPG models were proposed by combining the attention mechanism with Deep Deterministic Policy Gradient （DDPG） and Recurrent Deterministic Policy Gradient （RDPG）， respectively， and a PV power prediction framework was proposed on this basis. Firstly， the original PV power data and meteorological data were normalized， and the PV power prediction problem was modeled as a Markov Decision Process （MDP）， where the historical power data and current meteorological data were used as the states of MDP. Then the attention mechanism was added to the Actor networks of DDPG and RDPG， giving different weights to different components of the state to highlight important and critical information， and learning critical information in the data through the interaction of Deep Reinforcement Learning （DRL） agents and historical data. Finally， the MDP problem was solved to obtain the optimal strategy and make accurate prediction. Experimental results on DKASC and Alice Springs PV system data show that ADDPG and ARDPG achieve the best results in Root Mean Square Error （RMSE）， Mean Absolute Error （MAE） and R². It can be seen that the proposed models can effectively improve the prediction accuracy of PV power， and can also be extended to other prediction fields such as grid prediction and wind power generation prediction.