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.

Aiming at the problem of high matching cost in the existing complex event matching processing methods， a complex event matching algorithm ReCEP was proposed， which uses event buffers （ordered event lists） for recursive traversal. Different from the existing method that uses automaton to match on the event stream， this method decomposes the constraints in the complex event query mode into different types， and then recursively verifies the different constraints on the ordered list. Firstly， according to the query mode， the related event instances were cached according to the event type. Secondly， the query filtering operation was performed to the event instances on the ordered list， and an algorithm based on recursive traversal was given to determine the initial event instance and obtain candidate sequence. Finally， the attribute constraints of the candidate sequence were further verified. Experimental testing and analysis results based on simulated stock transaction data show that compared with the current mainstream matching methods SASE and Siddhi， ReCEP algorithm can effectively reduce the processing time of query matching， has overall performance better than both of the two methods， and has the query matching efficiency improved by more than 8.64%. It can be seen that the proposed complex event matching method can effectively improve the efficiency of complex event processing.