Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist physicians in diagnosing and grading pathological changes in pathologic myopia (PM). Clinically, due to the obvious differences in the position, shape, and size of the lesion structure in different scanning directions, ophthalmologists usually need to combine the lesion structure in the OCT images in the horizontal and vertical scanning directions to diagnose the type of pathological changes in PM. To address these challenges, we propose a novel feature interaction Transformer network (FIT-Net) to diagnose PM using OCT images, which consists of two dual-scale Transformer (DST) blocks and an interactive attention (IA) unit. Specifically, FIT-Net divides image features of different scales into a series of feature block sequences. In order to enrich the feature representation, we propose an IA unit to realize the interactive learning of class token in feature sequences of different scales. The interaction between feature sequences of different scales can effectively integrate different scale image features, and hence FIT-Net can focus on meaningful lesion regions to improve the PM classification performance. Finally, by fusing the dual-view image features in the horizontal and vertical scanning directions, we propose six dual-view feature fusion methods for PM diagnosis. The extensive experimental results based on the clinically obtained datasets and three publicly available datasets demonstrate the effectiveness and superiority of the proposed method. Our code is avaiable at: https://github.com/chenshaobin/FITNet.