Due　to　the　existence　of　small　inter-class　differences　and　large　intra-class　variance　among　fine-grained　images,　the　existing　classification　algorithms　only　focus　on　the　extraction　and　representation　learning　of　salient　local　features　of　a　single　image,　ignoring　the　local　heterogeneous　semantic　discrimination　information　between　multiple　images,　difficult　to　pay　attention　to　the　subtle　details　that　distinguish　different　categories,　resulting　in　the　lack　of　sufficient　discrimination　of　the　learned　features.　This　paper　proposes　a　progressive　network　to　learn　the　information　of　different　granularity　levels　of　the　image　in　a　weakly　supervised　manner.　First,　attention　accumulation　object　localization　module　(AAOLM)　is　constructed　to　perform　semantic　target　integration　localization　on　attention　information　from　different　training　epochs　and　feature　extraction　stages　on　a　single　image.　Second,　a　multi-image　heterogeneous　local　interactive　graph　module　(HLIGM)　is　designed　to　construct　a　graph　network　and　aggregate　information　between　the　local　region　features　of　multiple　images　under　the　guidance　of　the　category　label　after　extracting　the　salient　local　region　features　of　each　image　to　enhance　the　discriminative　power　of　the　representation.　Finally,　the　optimization　information　generated　by　HLIGM　is　fed　back　to　the　backbone　by　using　knowledge　distillation　so　that　it　can　directly　extract　features　with　strong　discrimination,　avoiding　the　computational　overhead　of　building　the　graph　in　the　test　phase.　Through　experiments　on　multiple　data　sets,　it　proves　the　effectiveness　of　the　proposed　method,　which　can　improve　the　fine-grained　classification　accuracy.　©　2024　Science　Press.　All　rights　reserved.