To solve the problems of low disparity accuracy in local stereo matching, a secondary guided filtering model is proposed and applied to the local stereo matching algorithm. The newly designed secondary guided filtering model overcomes the deficiency of traditional guided filtering and further suppresses the noises because the result of the first guided image filtering is used as the guiding image of the second guided filtering. In the cost aggregation phase, the introduction of the secondary guided filtering further improves the matching accuracy because the cross-scale framework is used to aggregate the matching cost volume at each scale. The experimental results demonstrate that the local stereo matching algorithm based on the secondary guided filtering possesses a high accuracy in the detection of standard stereo image pairs on the Middlebury benchmark. Moreover, the temporal complexity of the cost aggregation phases is independent of the filtering kernel size, and the proposed algorithm achieves good performances in speed and accuracy. The idea of the secondary guided filtering has potential applications in stereo matching and others.