We　present　an　automatic　classification　algorithm　for　retinal　optical　coherence　tomography　(OCT)　images　based　on　convolution　neural　network　(CNN).　This　algorithm　inherently　contains　feature　extraction　and　classification,　thus　avoiding　the　design　feature　extractor　manually.　Firstly,　we　processed　the　OCT　images　to　focus　on　and　determine　the　pathological　area　of　the　retinal　OCT　images,　and　to　speed　up　the　training　of　the　network.　Then　we　input　the　original　images　to　crop　them,　which　can　effectively　prevent　the　noise　introduced　in　the　processes　of　image　processing　and　changing　the　pixels　in　the　original　image.　Secondly,　we　augmented　the　OCT　images　in　the　source　data　set　to　obtain　sufficient　images,　and　to　alleviate　the　impact　of　a　relatively　small　number　of　target　classification　images　on　the　model　accuracy　and　generalization　ability.　Our　method　was　introduced　the　random　translation　in　image　cropping　and　horizontal　flipped　to　augment　the　OCT　images.　Then　we　applied　two　methods　to　build　two　data　sets　used　to　train　the　network,　and　we　divided　each　of　the　data　sets　into　a　training　set　and　a　validation　set.　Next,　we　designed　an　efficient　classification　network　and　trained　it　with　the　two　training　sets　respectively,　to　acquire　the　two　models　that　can　classify　OCT　images.　The　results　indicate　that　the　network　trained　by　the　augmented　data　can　classify　images　more　effectively.　In　our　classification　algorithm,　the　accuracy,　the　sensitivity　and　the　specificity　are　93.43%,　91.38%,　and　95.88%,　respectively.