Addressing the issues of existing deep learning-based direction of arrival (DOA) estimation algorithms, which suffer from large parameter volumes and dependence on covariance matrix inputs that are easily affected by noise, making deployment on resource-constrained edge devices challenging, this paper proposes a lightweight convolutional classification-regression neural network DOA estimation algorithm. The proposed method uses raw signals as model inputs and directly extracts DOA features from time-domain signals through end-to-end learning, thereby avoiding the performance degradation associated with traditional covariance matrix methods under low signal-to-noise ratio conditions. The model reduces the number of parameters through spatiotemporal feature compression and the integration of a Ghost bottleneck structure, and introduces an attention mechanism to adaptively recalibrate feature channel weights, enhancing focus on critical features. A dual-branch output strategy combining coarse classification and fine regression is adopted, first determining the angular interval and then predicting the sectoral offset, allowing for high-precision estimation even under stringent conditions (e.g., -5 dB SNR). Experimental results demonstrate that the model maintains outstanding performance (accuracy of 96.3%) while achieving high compactness (actual deployment size of 118.83 kB, with 24 783 parameters). Compared with traditional algorithms and mainstream lightweight models, this model preserves both accuracy and computational efficiency while reducing model parameter volume, providing edge devices with a high-precision, low-latency, and low-resource-consumption DOA estimation solution.