Near-field acoustic direction of arrival (DOA) estimation remains a relatively unexplored challenge in array processing, particularly under noisy and reverberant conditions. This paper presents a novel near-field source localization method leveraging the acoustic intensity vector in the spherical harmonics (SH) domain. Unlike conventional pressure coefficients, SH-based inten-sity (SH – INT) coefficients enhance the DOA estimation accuracy by improving the distance between the modes at high frequencies. The proposed approach begins by modeling the sound pressure captured by a spherical microphone array (SMA) in the SH domain. The acoustic intensity vector is derived from the SH decomposition of sound pressure and acoustic velocity, effectively encoding directional and energy information. By analyzing the dependency of the intensity vector on location, a convolutional neural network (CNN) is trained to map these features to DOA co-ordinates (azimuth and elevation), even in challenging reverberant and noisy environments. Comprehensive evaluations conducted through simulations and real speech experiments demonstrate the efficacy of the proposed method. Results reveal a substantial reduction in root mean square error (RMSE) compared to state-of-the-art techniques, highlighting its potential for accurate near-field acoustic localization.