Tropical cyclones (TCs) occurrence can reorganize the surrounding mean sea level pressure (MSLP) field, but whether this process is encoded in climate network remains unclear. Here, we construct 10-day evolving undirected climate networks from 6-hourly MSLP anomalies over the Western North Pacific (WNP) based on temporal coherence and magnitude similarity of pressure series. Four network metrics characterize the spatial and temporal responses of TC-induced pressure field change, revealing track-aligned low-value bands and U-shaped lifecycle variations. A convolutional neural network (CNN) trained on these metrics distinguishes TC and non-TC windows, while saliency maps identify a Philippine Key Region (PKR) where network changes are most informative for classification. Composite analysis further shows that the PKR presents different underlying ocean-atmospheric conditions between TCs and non-TCs, including variations in the Western Pacific Subtropical High (WPSH) and convective activity. These environmental contrasts shape local pressure field organization and translate TC-induced disturbances into systematic network changes. This framework provides an interpretable network-based perspective for TCs occurrence detection.