Abstract: Targeting the frequent casing deformation issues during volume fracturing in unconventional reservoirs, it is urgent to establish an efficient real-time early warning mechanism to accurately reveal the dynamic communication between hydraulic and natural fractures, thereby preventing wellbore instability caused by excessive fracture aggregation near the wellbore. The multi-scale discrete wavelet transform (DWT) theory was introduced to denoise and analyze the high-frequency fracturing signals in the time-frequency domain. Combined with the Nolte-Smith diagnostic theory and downhole microseismic monitoring data, a dynamic fracturing analysis and fracture growth feature extraction model based on frequency-band energy characteristics was constructed. Field applications in typical gas wells show that the model can accurately capture and quantify the energy mutation signals of fractures in the time-frequency domain. It effectively extracts the abnormal growth features of complex fracture networks (e.g., fault activation and uncontrolled fracture height), thereby achieving real-time diagnosis and early warning of casing deformation risks. The research indicates that this diagnostic and prediction method not only overcomes the poor real-time performance of traditional approaches but also significantly improves the monitoring capability of wellbore integrity during fracturing. It provides reliable engineering support for the dynamic optimization of fracturing parameters and the prevention of casing damage in the field.