This paper presents a data mining method combining improved genetic algorithm and association rules. Firstly, the crossover operator and mutation operator of genetic algorithm are improved adaptively so that they can adjust adaptively according to the fitness value of function in the process of iteration. The improved adaptive genetic algorithm is integrated into association rules to make full use of the good global search ability of genetic algorithm and improve the mining efficiency of association rules dealing with mass data. In order to avoid useless rules and reduce the existence of irrelevance, intimacy is added to improve the reliability of association rules. The optimized algorithm is verified by analyzing traffic data on Hadoop big data platform. Compared with traditional methods, this method improves the convergence speed and robustness of the algorithm.