To achieve more accurate sound event detection and localization in complex environments characterized by source overlap and strong noise interference, this study first conducts comparative experiments using multiple acoustic features as inputs to a deep learning network. The results indicate that LGAM (logarithmic Gamma spectrum) provides a richer representation of both high- and low-frequency information, and is therefore adopted as the input feature. Furthermore, considering the inherent temporal characteristics of acoustic signals, a novel attention-based architecture, termed ACANet (asymmetric convolutional attention network), was proposed. ACANet employs asymmetric convolution kernels to enhance the learning of temporal dependencies and integrates CCAM (coordinate channel attention module) to improve the modeling of channel and spatial information. Experiments conducted on the TAU-NIGENS Spatial Sound Events 2020 dataset demonstrate that, compared with the baseline, the proposed method achieves overall performance gains, reducing ER (error rate) by 5%, decreasing LE (localization error) by 1.9°, and improving F₁ score and FR (frame recall) by 3.1% and 1.4%, respectively. These findings confirm the effectiveness and application potential of the proposed approach for enhancing SELD(sound event localization and detection) performance in complex acoustic environments.