In response to the issues of poor adaptability to low signal-to-noise ratios (SNRs) in existing uniform linear array (ULA) multitarget estimation algorithms and the difficulty of current deep learning methods in effectively extracting complex-valued features from data, a cross-scale sparse attention module and a channel-hierarchical spatial pyramid attention module, which are based on the MSPANet block, are introduced into the deep neural network (DNN). This approach better extracts multiscale features of signalling components, facilitating accurate signal feature extraction under low SNR conditions. Experimental data demonstrate that this deep learning model can significantly enhance the accuracy and anti-jamming capability of direction-of-arrival (DOA) estimation in low-signal-to-noise ratio (SNR) scenarios, outperforming traditional methods such as CBF, MUSIC, and ESPRIT. The above optimization achievements possess important practical value for DOA estimation applications in fields like intelligent speech, radar detection, communication systems, and autonomous driving.