The extraction and analysis of ship flow field characteristics have long been a critical research direction in hull form design and ship condition analysis. However, data obtained through numerical simulation methods often remain underutilized, and traditional methods suffer from limited feature representation capabilities. This paper proposes a deep learning-based approach for analyzing the characteristics of pressure time-series data on ship surfaces. A clustering autoencoder model is constructed to perform both feature dimensionality reduction and feature classification. Using the spectral curves of pressure time-series data from propeller blade surfaces as training data, the model extracts low-dimensional features for subsequent classification. This process effectively visualizes the spatial distribution of flow field pressure time-series characteristics and accurately identifies high-frequency measurement points. The findings validate the utility of deep learning in the analysis of ship flow field characteristics and provide a methodological framework for understanding flow field characteristics induced by propeller excitation.