Existing　association　rule　recommendation　technologies　were　focus　on　extraction　efficiency　of　association　rule　in　data　mining.　However,　it　lacked　consideration　of　recommendation　balance　between　popular　and　unusual　data　and　efficient　processing.　In　order　to　improve　the　quality　and　efficiency　of　personalized　recommendation　and　balance　the　recommendation　weight　of　cold　and　hot　data,　the　problem　of　mining　frequent　itemset　based　on　association　rule　was　revaluated　and　analyzed,　a　new　evaluation　metric　called　recommendation　RecNon　and　a　notion　of　k-pre　association　rule　were　defined,　and　the　pruning　strategy　based　on　k-pre　frequent　itemset　was　designed.　Moreover,　an　association　rule　mining　algorithm　based　on　the　idea　was　proposed,　which　optimized　the　Apriori　algorithm　and　was　suitable　for　different　evaluation　criteria,　reduced　the　time　complexity　of　mining　frequent　itemset.　The　theoretic　analysis　and　experiment　results　on　the　algorithm　show　that　the　method　improved　the　efficiency　of　data　mining　and　has　higher　RecNon,　F-measure　and　precision　of　recommendation,　and　efficiently　balance　the　recommendation　weight　of　cold　data　and　popular　one.　©　2017,　Editorial　Board　of　Journal　on　Communications.　All　right　reserved.