Morphological filtering (MF) is a typical fault feature extraction technology, which has been widely used in rolling bearing fault diagnostics. The structural element (SE) length has an important influence on feature extraction and noise removal, and the filtering result obtained by the filtered signal under a single SE length or the weighted average of different filtered signals under partial SE lengths is limited and insufficient. To address this problem, a transient feature extraction method called improved time-varying morphological filtering (ITVMF) is proposed for bearing fault diagnosis, wherein the SE length is adaptively determined according to the inherent characteristics of vibration signals. Moreover, the autocorrelation envelop spectrum (AES) is employed to further eliminate fault-unrelated components. The main innovation of this method is to automatically extract bearing fault-related transient features using a novel time-varying SE (TVSE) based on autocorrelation while effectively eliminating the interference of random impulses and strong background noise on bearing fault features. The developed ITVMF-AES is investigated and validated on the simulation signals, accelerated bearing degradation data, and artificial experimental test data, and compared with three existing feature extraction methods. The results demonstrate that the developed methodology is more efficient in excavating transient impulse features and identifying bearing faults than the comparison methods, and has potential engineering application value.