This paper addresses the challenge of analyzing complex abnormal waveforms generated by power transformers and current transformers （CT） during core saturation. It proposes an intelligent recognition and restoration method for transformer complex abnormal recorded waveforms based on cascaded neural networks. The proposed method enables rapid and efficient identification of abnormal types from abnormal recordings and accurately extracts their fundamental components. First，a mechanism model for transformer inrush currents and CT saturation is established to analyze key influencing factors of these abnormal waveforms. Subsequently，a cascaded convolutional neural network （CNN） framework is constructed，featuring a primary network for abnormal waveform classification and a secondary network dedicated to identifying unsaturated regions within inrush and CT saturation currents. This cascading architecture effectively decomposes the waveform analysis task. Next，based on the identified unsaturated segments of the waveforms，the inrush and CT-saturation currents are restored using the least-squares method，yielding their fundamental components. Finally，the method’s performance is rigorously verified using simulated waveforms. The results demonstrate that the proposed method significantly enhances the efficiency and accuracy of abnormal recording analysis compared to conventional approaches.