In the course of mining frequent neighboring class set, present algorithms have some redundant candidate and repetitive computing, which are only able to efficiently extract short frequent neighboring class set, and so this paper proposes an algorithm of fast duplex mining frequent neighboring class set, which is suitable for mining any frequent neighboring class set. This algorithm adopts two methods to generate candidate frequent neighboring class set in the mining course. One is using top-down search strategy to generate candidate by numerical index, the other is using anti-code of front this candidate to generate next candidate frequent neighboring class set. The course of top-down search strategy used by the algorithm isn’t different from traditional top-down search strategy, which uses numerical index to generates k-subset of (k+1)-non frequent neighboring class set. By the two methods, the algorithm may delete redundant candidate and repetitive computing. The algorithm creates digital database of neighboring class set via neighboring class weight, according to character of database, it also uses digit logical operation to computes support. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining frequent neighboring class set in large spatial data.