With the development of information technology, traditional network transmission and equipment fault monitoring suffer from high false alarm and missed alarm rates. In complex network environments, it is difficult to identify faults timely and effectively. To address this issue, this study proposes a fault monitoring system that combines information visualization and human-computer interaction technologies through the integration of compressed sensing (CS) and the sliding window (SW) method. Experimental results demonstrate that the system based on the CS + SW method achieves a fault image information conversion accuracy of 85.29%, significantly higher than the 66.37% of traditional methods and 78.22% of the Support Vector Machine (SVM) method. The misjudgment rate of CS + SW is only 9.07%, much lower than other comparative algorithms, indicating its high efficiency and stability in practical applications. The visual communication effect of CS + SW is significantly enhanced, improving from 45.3% to 91.28%. The CS + SW method not only improves diagnostic accuracy but also enhances the clarity and accuracy of image communication. In summary, CS + SW can effectively improve the accuracy and real-time performance of network transmission fault monitoring in complex and highly dynamic network environments, exhibiting high practical value. This research provides new ideas and methods for future fault diagnosis systems and demonstrates good potential in information visualization and human-computer interaction technologies for fault monitoring.