Abstract: In recent years, the rapid accumulation of massive digital seismic observations has created an urgent demand for efficient and intelligent data-processing methods. This paper presents a suite of new intelligent approaches to seismic data processing developed by our research group, including: POSE, an automatic P-wave first-motion polarity determination method based on Order Statistics and Entropy theory; an automatic detection algorithm for fault zone head waves designed to identify bimaterial interfaces; and HiFi, a method for detecting remote dynamic triggering based on the High-Frequency power Integral ratio. These methods not only significantly enhance the resolution of focal mechanism solutions and stress field inversions for small earthquakes, but also provide new tools for characterizing medium contrasts across faults and for investigating how dynamic stress perturbations modulate small earthquake activity. Through application to the 2023 Türkiye earthquake doublet and the 2025 Mw 7.7 Mandalay, Myanmar earthquake, we demonstrate the advantages of POSE in focal mechanism determination and regional stress field inversion, the effectiveness of fault zone head-wave detection in revealing velocity contrasts across bimaterial fault interfaces, and the robustness of the HiFi method in identifying long-range dynamic triggering associated with large earthquakes. These new observations offer important support for fault-zone structural imaging, rupture dynamics studies, and seismic hazard assessment, and highlight the broad prospects of intelligent techniques in seismological research.