Drifting fish aggregating devices （DFAD） have been widely used in tuna purse seine fisheries， and their large-scale deployment may alter fish aggregation behavior and influence the distribution of unassociated tuna schools. However， systematic analyses of the impacts of DFAD on the distribution of unassociated tuna schools remain lacking， and related predictive models are still limited. Based on purse seine fishery data from the Western and Central Pacific Ocean， this study assumes relatively stable fishing efficiency and adopts catch per unit effort （CPUE） as an indicator of the catch level of unassociated schools， proposing a “background field + residual” decomposition framework. First， a machine learning model integrating environmental variables and temporal features was developed to construct the environmental background field of unassociated school CPUE， and the residual was defined as the difference between observed CPUE and the background field. DFAD features with different temporal lags and spatial neighborhood scales were then constructed， and a random forest model was used to identify spatiotemporal feature combinations with greater predictive contribution for residual prediction. Based on the selected features， a spatio-temporal multi-task model was further developed to predict CPUE residuals， and ablation experiments were conducted to evaluate the contribution of DFAD features. The results showed that DFAD features exhibited varying predictive performance at different temporal lags， with features with features at lags of 3， 4 and 5 months showing higher importance than those from the 2nd previous month. The proposed model achieved an accuracy of 71.54% in the residual binary classification task， outperforming the comparison model without DFAD related features. These findings indicate that the proposed decomposition framework can integrate environmental and DFAD features， effectively improving the prediction of anomalous variations in unassociated school CPUE.