## 1 Introduction  
The purpleback flying squid (_Sthenoteuthis oualaniensis_) is the primary economic and commercial species targeted by the high seas light-purse seine fishery in the Northwest Indian Ocean. It is also widely distributed in the subtropical waters of the Pacific Ocean [1], with global resources estimated at approximately 10 million tons [2]. China began exploring the purpleback flying squid resource in the Northwest Indian Ocean in 2003 [2, 3],and commercial light-purse seine fishing vessels began operations in this region in 2014. The investigation showed that the catch per unit effort (CPUE) of this species exhibited an increasing trend from 2016 to 2019. However, there was a significant decline in 2020 [4]. Two major factors likely contributed to this decline: a sharp increase in fishing pressure, perhaps due to more intensive fishing activities, and climate change, which can disrupt marine ecosystems and species' availability [5]. Therefore, accurately predicting the fishing grounds of the purpleback flying squid in the Northwest Indian Ocean is essential for ensuring sustainable fishery production. The purpleback flying squid, with its short life cycle, is a crucial warm-water pelagic cephalopod that, similar to other cephalopods, can be significantly affected by SST [6; 7]. Research on cephalopods has indicated that temperature influences various biological and ecological parameters, including their growth length, sex ratio, body size, and population density [8]. However, there are differences in the effects of SST on cephalopods during the incubation period, juvenile, and adult stages. [PERSON] et al. [9] developed a prediction model based on SST and concluded that SST has a significant effect on the aggregation behavior of juvenile _lllex argentines_ and also concluded that SST during the incubation period is negatively correlated with the catch of _I. argentines_ in the following season, while the study of [PERSON] [10] also demonstrated that the difference in temperature during the early growth of _Loligo gahi_ will result in the significant difference in body size of the adult squid. In addition, seawater temperature anomalies due to climatic change also significantly affect the distribution of cephalopods. [PERSON] et al. [5] studied the effects of the El Nino index and regional water temperature on _Ommastreples batrramii_ in the Northwest Pacific Ocean and _Dosidicus gigas_ in the Southeast Pacific Ocean. They concluded that the El Nino phenomenon can change the favorable SST area and the optimal oceanic fronts, which results in the abnormal distribution of the two species of squid. Therefore, studies on SST and climate-induced SST anomalies on cephalopods are conducive to deepening the comprehensive knowledge of cephalopod resources and can provide an important reference for fishery resource management and model construction of fishing ground predictions. Machine learning (ML) is a scientific discipline that focuses on the study of algorithms derived from data. It has been extensively applied in fisheries to predict fishing vessel behavior or CPUE [11; 12]. ML models are generally regarded as possessing high accuracy and efficiency. For instance, [PERSON] et al. [13] utilized sea surface temperature (SST) and sea surface height (SSH) to develop a prediction model based on the habitat index, achieving an average accuracy of 63%. Similarly, [PERSON] et al. [14] created a prediction model using the Bayesian analysis, which yielded an accuracy of approximately 65%. Although traditional machine learning methods generally show high training speeds and strong interpretability, their simplistic model structures present challenges in effectively accommodating large-scale fisheries and environmental datasets. As a result, the prediction accuracy of these models tends to be relatively low [11; 12]. In contrast, deep learning can extract potential features from vast amounts of data, revealing hidden patterns and high-order feature information [15].