Optical fiber sensing technology is pivotal for real-time monitoring of converter valve status, which directly influences the operational stability of the entire converter valve system. However, the absence of comprehensive evaluation standards has hindered the development of an effective and reliable assessment framework for optical fiber sensing in this domain. In this study, we propose a multi-dimensional integrated operational evaluation method for optical fiber sensing in converter valves, which leverages the analytic hierarchy process (AHP) and a high-order backpropagation (HO-BP) neural network. The methodology begins with a systematic identification and generalization of key operational indicators for fiber-optic monitoring sensors. Subsequently, relevant metrics are refined and prioritized using AHP with the assistance of yaahp V10.3 software. Finally, a HO-BP neural network model is constructed to perform a comprehensive assessment of the operational status of optical fiber sensors in converter valves. Experimental results demonstrate that the proposed method achieves an overall accuracy of approximately 95.0%. Furthermore, in terms of overall accuracy, recall for abnormal cases, and recall for severe cases, the proposed approach consistently surpasses benchmark models such as Random Forest (RF), Long Short-Term Memory (LSTM) networks, conventional BP neural networks, and decision trees. These results collectively validate the effectiveness and superiority of the proposed evaluation strategy.