Visual characteristics have the potential to assess the navigational proficiency of ship pilots. A precise assessment of ship piloting competence is imperative to mitigate human errors in piloting. An exhaustive examination of cognitive capabilities plays a pivotal role in developing an enhanced and refined system for classifying, selecting, and training ship piloting proficiency. Insufficiency in situation awareness (SA), denoting the cognitive underpinning of hazardous behaviors among pilots, may lead to subpar performance in ship pilotage when faced with adverse conditions. To address this issue, we propose an SA recognition model based on the random forest-support vector machine (RF-SVM) algorithm, which utilizes wearable eye-tracking technology to detect pilots’ at-risk cognitive state, specifically low-SA levels. We rectify the relative error (RE) and root mean square error (RMSE) and employ principal component analysis (PCA) to enhance the RF algorithm, optimizing the combination of salient features in greater depth. Through the utilization of these feature combinations, we construct a SVM algorithm using the most suitable variables for SA recognition. Our proposed RF-SVM algorithm is compared to RF or SVM alone, and it achieves the highest accuracy in recognizing at-risk cognitive states under poor visibility conditions (an improvement of 86.79% to 93.43% in accuracy). Taken collectively, the present findings offer vital technical support for developing a technique-based intelligent system for adaptively evaluating the cognitive accomplishment of pilots. Furthermore, they establish the groundwork and framework for the surveillance of cognitive processes and capabilities in marine pilotage operations within China.