To support the low-carbon and digital transformation of fisheries and to address the strong intercorrelation of vertical dissolved oxygen （DO） profiles in fishing ground prediction，this study focuses on the Dosidicus gigas fishery off Peru in the Southeastern Pacific Ocean and investigates the applicability of objectively identifying key DO layers for intelligent fishing ground prediction. Based on fishing operation data from 2014 to 2021 and DO profile data from 0 to 200 m depth， a self-organizing map （SOM） combined with hierarchical cluster analysis was employed to perform nonlinear dimensionality reduction on 31 vertical DO layers， extract the nonlinear features of the vertical DO structure， and identify representative key layers. On this basis， climate factors， including the Pacific Decadal Oscillation （PDO） and Ni?o 1+2 index， were further integrated to construct Random Forest （RF）， Gradient Boosting Regression Tree （GBRT）， and TabPFN models， and to compare the predictive performance and spatial distribution characteristics of CPUE under full-profile and reduced-feature DO representation schemes. The results demonstrate that： （1） two key DO layers at depths of 23 m and 147 m can be stably identified through vertical DO structure analysis； （2） the reduced-feature scheme enables effective simplification of DO information without a significant loss of prediction accuracy； and （3） the spatial distribution of DO in the key layers is consistent with the high-value areas of jumbo flying squid CPUE， and CPUE is positively correlated with DO in the key layers. These findings provide a reference for intelligent fishing ground prediction and fishing decision-making based on ocean environmental monitoring data.