Abstarct: Abstract Markets are getting more saturated every day and the competition between businesses is increasing, that's why customer loyalty management has become very important in various businesses. With the expansion of data science and data mining, businesses moved towards the use of intelligent knowledge extraction systems, and customer behavior and loyalty prediction systems are one of the most important of these systems. These systems extract patterns for predicting customer behavior by using databaxses recorded from customer interactions and help managers determine business policies to retain customers In this research, with the aim of identifying the influencing factors on customer loyalty during the post-warranty period and evaluating them, a questionnaire consisting of influencing factors was prepared with the help of experts. The questionnaires were completed by the customers of Iran Khodro dealerships and combined with the customer records dataset. Using data mining algorithms and different decision trees to classify customers, predict customer loyalty behavior and evaluate factors affecting their loyalty. The results of the research showed that to classify and predict customer loyalty in the post-warranty era, the decision tree with criteria Gain Ratio and k-fold cross validation(k=10) Technique compared to other algorithms such as KNN and Naïve Bayes, it has higher accuracy and precision, and by using weighting algorithms, it was determined that the indicators of repair descxription, good history of repairs, personnel expertise and more proficiency, knowledge of the benefits of repairs in the car dealer and Knowledge of warranty terms and conditions has the greatest effect on customer loyalty during the post-warranty period.