© Published under licence by IOP Publishing Ltd. Tree ring analysis is an important field of science, and is vital in modeling the environmental response system of tree growth. In most cases, analyses have been conducted using one parameter from one tree ring, e.g., ring-width, density, or ratio of stable isotopes. The information within a ring, however, has been less studied, although it offers many more possibilities for investigation, such as seasonal responses over shorter time scales. Therefore, to elucidate the sub-seasonal climatic response of softwood (Cryptomeria japonica), we investigate the use of a wavelet-convolutional neural network (CNN) model, which incorporates spectral information that is normally lost in conventional CNN models. This paper highlights the usefulness of the wavelet-CNN for classifying cross-sectional optical micrographs and extracting structural information specific to a calendar year. Class activation maps indicate that the dimension and position of cells in a radial file are likely to be discriminative features for the wavelet-CNN. This study shows that wavelet-CNNs have the potential to be highly effective methods for dendrochronology.