As the earliest mature Chinese character system, oracle bone inscriptions (OBI) contain valuable historical and cultural information, making their image retrieval techniques crucial for text interpretation and cultural heritage digitization. However, structural variations caused by inscriptions, burial conditions, and topography, along with high frequency noise and edge blurring in the images, pose significant challenges to accurate retrieval. To address these challenges, this paper proposes a frequency-aware and stroke-enhanced multiscale hash network (FSMHNet). The method introduces fast Fourier transform (FFT) in the feature extraction stage to map features into the frequency domain, then filters and adjusts different frequency components through a weighted gating mechanism. Building on this foundation, we combine discrete wavelet transform (DWT) to achieve multiscale decomposition and design an asymmetric pyramid convolution module (APC) to capture feature differences between horizontal and vertical strokes of oracle bone characters, thereby improving feature orientation sensitivity. Additionally, we design a stroke feature enhancement (SFE) module to further strengthen the semantic expression of local key strokes, while incorporating the multi-head self-attention mechanism (MHSA) to model global associations and enhance overall semantic representation. Finally, the system outputs efficient retrieval representations through hash coding. Experimental results demonstrate the method’s effectiveness and generalization capability in both same-domain and cross-domain retrieval tasks constructed on the Oracle-MNIST and Oracle-241 datasets.