In　order　to　improve　feature　extraction　capabilities,　this　work　explores　a　new　approach　for　picture　recognition.　It　combines　cutting-edge　deep　learning　algorithms　with　traditional　image　processing　methodologies.　First　of　all,　we　provide　a　thorough　historical　analysis　of　the　development　of　conventional　image　processing　techniques　and　carefully　examining　both　their　inherent　advantages　and　disadvantages.　Then　we　provide　a　novel　feature　extraction　framework　that　synergistically　combines　conventional　methods　with　deep　learning,　which　enhances　the　model＇s　ability　to　extract　complex　picture　features　via　multilevel　feature　synthesis.　To　further　improve　the　model＇s　recognition　effectiveness,　we　also　investigate　the　use　of　data　augmentation　and　transfer　learning　techniques.　Empirical　evaluations　conducted　across　multiple　standard　datasets　corroborate　the　efficacy　of　the　proposed　framework,　positioning　it　competitively　against　contemporary　state-of-the-art　methodologies.　The　experimental　outcomes　clearly　illustrate　that　our　proposed　method　not　only　adeptly　manages　complex　imagery,　noise,　and　occlusions　but　also　significantly　enhances　generalization　and　computational　performance.　The　insights　garnered　from　this　research　contribute　novel　perspectives　and　methodologies　to　the　domain　of　image　recognition,　offering　substantial　reference　values　for　the　application　of　deep　learning　principles　in　alternative　fields.　©　2025　IEEE.