Pathologic myopia is one of the common eye diseases, which is becoming more severe with the increase of myopia prevailing around the world. The diagnosis of pathologic myopia and its co-existing myopic maculopathy is crucial but usually not timely enough due to the lack of experienced ophthalmologists. Therefore, we propose a new dual-branch architecture to detect pathologic myopia and classify myopic maculopathy named DBA-PMC. This architecture can be applied with any feature extraction backbone and can give pathologic myopia prediction and maculopathy classification at the same time. Because pathologic myopia and myopic maculopathy labels are corelative, two branches of the DBA-PMC can mutually promote the performance of each other through comprehension of this correlation. With extensive experiments, the DBA-PMC surpasses baseline methods with Acc 99.12% for pathologic myopia prediction and mAP 86.095% for maculopathy classification. This work can help screen and diagnose pathologic myopia and alleviate the work of ophthalmologists.