Large roller coasters, renowned for their colossal dimensions, varied designs, and intricate motion dynamics, primarily appeal to teenagers and children. Malfunctions in these structures can gravely threaten lives and have significant societal ramifications. Their intricate structures and swift dynamic behavior expose load-bearing components to multi-axial alternating complex loads, rendering them susceptible to weld-site cracks. Timely detection of these defects is crucial, as undetected issues can rapidly progress to structural failure, leading to catastrophic accidents. Currently, inspections of large amusement facilities predominantly rely on traditional periodic manual inspections, which though ensuring initial safety, pose challenges in sustaining reliable and safe operation over prolonged usage. Notably, there are several major issues in stress testing for roller coaster. Firstly, accurately determining hot-spot stress is challenging, as theoretical high-stress locations may differ from reality. Moreover, structural complexity restricts direct measurement of maximum stress points. Secondly, damage prediction remains elusive, as stress testing is limited to short-term data, lacking long-term monitoring and a defined relationship between stress states and damage, thus impeding effective damage forecasting. Hence, this study emphasizes the implementation of stress monitoring at readily accessible locations within the stress concentration zones of roller coaster’s critical and intricate structures, aiming to gather long-term observational data. Utilizing these comprehensive datasets, a stress inversion method is executed at the high-stress hot-spots, thereby reconstructing the hot-spot stress field. This approach enables proactive warning mechanisms and condition-based maintenance strategies for complex structural failures, thereby holding immense practical significance in guaranteeing the safe and dependable operation of large-scale amusement facilities.