## 1 Introduction  
With the global trend and people's attention, how to monitor the air quality scientifically and effectively and how to further prevent and control air pollution has become a hot topic. The problem of air pollution is very complex, which is characterized by multi pollution coexistence, multi-scale correlation, and multi process evolution. In order to solve this complex problem, it is particularly important to strengthen the construction of air quality monitoring and air quality management information. Only effective prediction, analysis, and research on air quality can effectively improve air quality. How to effectively use the real-time monitoring data of each city's automatic air monitoring station, mine its internal information, use the monitoring data to build a bridge for analyzing the pollution problem [1], effective improvement of air quality, improvement of people's living environment to maintain people's health is an urgent problem to be solved.  
The air quality index (AQI) is calculated by monitoring the concentration of fine particulate matter (PM\({}_{2.5}\)), inhalable particulate matter (PM\({}_{10}\)), sulfur dioxide (SO\({}_{2}\)), nitrogen dioxide (NO\({}_{2}\)), ozone (O\({}_{3}\)), and carbon monoxide (CO). In recent years, due to the increasing consumption of various energy resources and the increase of cumulative emissions, the problem of air pollution has seriously increased. More and more attention has been paid to the study of air pollution. In order to better adapt to the global trend and create a good air environment, using data mining technology to establish air quality analysis, the forecast model has become an important topic [2; 3; 4; 5; 6; 7; 8; 9].  
Collecting AQI data is key for monitoring pollution problems. To solve the AQI problem, various approaches have been used for data mining, including artificial neural network (ANN), genetic algorithm (GA), decision tree (DT), random forest (RF), and support vector machine (SVM) [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Each method has a single basic point of view and provides a general performance analysis of air quality indicators, but it is difficult to distinguish the best method. Recent studies have proposed various intelligent systems, and the results seem applicable [21; 22; 23; 24; 25; 26]. However, these investigated methods have an important shortcoming: they cannot simultaneously provide parameter optimization for an algorithm as well as decision rules. For AQI evaluation, decision rules can be updated according to datasets in the evaluation process and can be used to predict new evaluation results. For that we would aim to investigate an algorithm based on the characteristics of AQI decision rule establishment and parameter optimization. Then the urban air quality forecast and analysis can be based on an intelligent algorithm with parameter optimization and decision rules. We therefore propose an intelligent algorithm that combines DT and simulated annealing (SA), in which DT generates decision rules and SA converges to a global optimum, and the parameters of DT are determined by SA. The rules extracted in this paper could be used to analyze collected information, then forecast a new AQI. In what follows, we review the decision tree in Section 2, introduce the proposed algorithm in Section 3, and analyze the simulation results and discussions in Section 4. Finally, we draw the conclusion.