Aiming at the problems caused by intermittent faults of ground air conditioning, including low use efficiency, maintenance lag etc., a prediction method of intermittent faults which combined re-association Array Summation (AS)-Apriori with clustering K-means was raised, based on this method, delayed maintenance forecast was realized. The low efficiency problem of frequently scanning transaction database in Apriori was solved in AS-Apriori algorithm, by constructing intermittent fault arrays and giving a summation of corresponding items on them in real-time. The goal of delayed maintenance forecast is to estimate the critical region of permanent fault to arrange reasonable maintenance, which can be realized by using Gaussian distribution for the solution of maintenance wave of different intermittent fault variables and delay probability and then giving an accumulation in order. The results show that, the operational efficiency is improved, the support degree of re-association rules is upgraded by 20.656 percentage points, and more accurate prediction of intermittent failure is realized. Moreover, according to the analysis of data, the probability of forecasting maintenance-wave and delay-probability is shown as a linear distribution, which means that the high predictability of intermittent faults is more convenient to maintain and manage in advance and the formation of permanent fault is reduced.