Apriori semantic processing orienting at the business English parallel corpus characteristics in business contract

English grammar teaching is an indispensable part of language teaching, and learners’ mastery and use of grammatical knowledge have a positive and important role in obtaining speech ability. Aiming at the problem of characteristic processing in business English parallel corpora in business contracts, this paper proposes a similar-Apriori mining semantic processing algorithm under two-stage high expectation weight business English parallel corpus item set. First of all, aiming at the problem that in the traditional hierarchical mining algorithm, only business English parallel corpus item set upper bound is used, which has not ideal effect for the high expectation weight problem, the downward closure characteristics of hierarchy mining algorithm is designed, and the proof procedure is given, this characteristic can effectively reduce the processing quantity of the candidate business English parallel corpus while ensuring the accuracy. Secondly, the two-stage data mining process is constructed, in the first stage, a high expectation weight business English parallel corpus item set is obtained for a set of candidate business English parallel corpus item set based on a hierarchical search method, in the second stage, the database is scanned again to obtain high expectation weight of business English parallel corpus item set, so as to complete the data mining process. Finally, the effectiveness of the algorithm is verified through simulation experiment.