When the correlation between variables is difficult to measure by correlation coefficient, the association rules can be used to examine the relationship between the variables. The possibility of application that association rules in psychological measurement is analyzed in theoretic, then using the Apriori algorithm for mining frequent item sets to analyze cross-cultural adaptation data of foreign students. Determined questionnaire topics by interview method, then determined the formal questionnaire by exploratory factor analysis. In accordance with the international students Language, translated the formal questionnaire into English, Japanese and Korean, then distributed these formal questionnaire to 11 provinces including 16 universities, eventually obtained 651 valid questionnaires. The formal questionnaire is divided into two parts: adaptation influencing factors and adaptation level, in which adaptation influencing factors including demographic factors, social and cultural influencing factors, the school environment influencing factors and personality psychological characteristics. The Cronbach consistency coefficients of each part of the formal questionnaire is greater than 0.7. Used the data mining software Weka to implement Apriori algorithm, and set the minimum support "minsup" to 0.2, while the lowest confidence level not less than 0.6, then extracted information, and used the lift rate to evaluate the rules. Compare the results between association rules, Pearson correlation and Spearman relevant, founding that using the association rules can be more simple and effective to get the rules between variables. The results show that association rules can gain the support and confidence of each level of the variables; it can deduce the rules between the multivariate; it is very effective for dealing with the correlation in non-linear multi-variable and multi-categorical variables. This is the first time for introducing association rules method to psychological measurement; it is a very useful method for analyzing the relationship between the data.