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Moisture plays a key role in the energetics of hurricanes. Using a convolutional autoencoder, a state-of-the-art deep learning approach to spatial pattern classification, with k-means we identified four representative clusters of total column water vapor (TCWV) patterns around North Atlantic hurricanes. These four clusters exhibit distinct spatial distributions of TCWV in terms of amount, symmetry, and areal extent. Cluster 1 has a compact, symmetric, and moderate moisture pattern which we refer to as medium moisture symmetrical. Cluster 2 is high moisture symmetrical as these hurricanes have an abundance of moisture with a widespread and symmetric pattern. Cluster 3 is low moisture asymmetrical as it represents the driest conditions especially in the northwest. Cluster 4 has high moisture near the center but exhibits a pattern with the strongest contrast between dryness in the northwest and wetness in the southeast, thus we label it high moisture asymmetrical. Each cluster has distinct geographical and temporal distributions, indicating differences in dynamic and thermodynamic environmental conditions associated with each cluster's moisture pattern. Additionally, hurricane intensity, size, and precipitation features vary among the four clusters, characteristics which are closely associated with the moisture and environmental conditions of each cluster. Our study's application of a deep learning method in classifying spatial patterns of moisture around hurricanes highlights the importance of moisture conditions in a hurricane's evolution.