Steam valves serve as critical fluid control components in the nuclear and chemical industries, where their health status directly impacts production safety. To address the limitations of general-purpose monitoring software—specifically blocking issues during multi-channel concurrent acquisition and the lack of specialized backends for complex models—this paper proposes a design scheme for fault diagnosis software based on the PyQt5 framework and the Model-Based Design paradigm. A dual-mode architecture featuring "real-time acquisition and offline analysis" is constructed. By incorporating an asynchronous double-buffering mechanism and QThread pooling, the system effectively overcomes performance bottlenecks caused by resource competition between synchronous acquisition and model inference. Based on operational data from main steam isolation valves at full power, an LSTM-SVM cascade hybrid model is established. This model employs LSTM to capture nonlinear temporal evolutionary patterns for feature prediction and SVM to delineate decision boundaries, thereby achieving collaborative diagnosis ranging from "situational awareness" to "trend warning." Experimental results demonstrate that the system supports non-blocking synchronous acquisition across 26 channels, maintains an alarm response time of ≤80 ms, achieves a fault determination accuracy of approximately 94%, and reduces troubleshooting time for typical faults by about 90%. Field applications validate the effectiveness of the proposed scheme, providing a robust solution for the specialized monitoring of steam valves.