The portfolio problem is a hot issue in the field of quantitative trading. An Integrated Deep Reinforcement Learning Portfolio Model （IDRLPM） was proposed to address the shortcomings of existing deep reinforcement learning-based portfolio models that cannot achieve adaptive trading strategies and effectively utilize supervised information. Firstly， multi-agent method was used to construct multiple base agents and design reward functions with different trading styles to represent different trading strategies. Secondly， integrated learning method was used to fuse the features of strategy network of the base agents to obtain the integrated agent adaptive to market environment. Then， a trend prediction network based on Convolutional Block Attention Module （CBAM） was embedded in the integrated agent， and the output of the trend prediction network guided integrated strategy network to adaptively select the proportion of trades. Finally， under the alternating iterative training of supervised deep learning and reinforcement learning， IDRLPM effectively utilized supervised information from training data to enhance model profitability. The Sharpe Ratio （SR） of IDRLPM reaches 1.87 and 1.88， and the Cumulative Return （CR） reaches 2.02 and 1.34 in Shanghai Stock Exchange （SSE） 50 constituent stocks and China Securities Index （CSI） 500 constituent stocks； compared with the Ensemble Deep Reinforcement Learning （EDRL） trading model， the SR improves by 105% and 55%， and the CR improves by 124% and 79%. The experimental results show that IDRLPM can effectively solve the portfolio problem.