An algorithm of weighted association rules mining for query expansion is proposed based on the evaluation framework of support-relevancy-chi-square analysis-confidence(SRCSAC). And the models of cross language query post-translation expansion(CLQPTE) are presented and a new computing method of the expansion term weight is given. Then, an algorithm of CLQPTE is proposed forward based on the SRCSAC framework mining. The algorithm uses the support-relevancy framework and the pruning method to mine effective frequent itemsets, and extracts the weighted association rules from the frequent itemsets in terms of the framework of chi-square-confidence. The high quality expansion terms are obtained from the association rules according to the expansion models in order to carry out CLQPTE. The experimental results show that the proposed algorithms can effectively restrain the issue of query topic drift and term mismatch. Compared with the benchmark retrieval, the MAP minimum average increases(MAIs) of the proposed antecedent expansion(AE), consequent expansion(CE) and hybrid expansion(HE) of the association rules are 86.85%,86.04% and 86.00%, respectively. Compared with the contrast methods, the MAP MAIs of the long queries retrieval for the proposed AE, CE and HE algorithms can reach 12.23%, 9.06% and 12.6%, respectively, which are all higher than those of the short queries retrieval. The MAP maximum increase of the AE and HE can be up to 5.5% compared with the CE algorithm. The confidence is helpful to improve the retrieval performance of the AE and HE algorithms, and the relevancy is more conducive to the improvement of retrieval performance of the CE. The support and relevancy are more effective for short queries retrieval based on the CE algorithm.