Near-field channel estimation is a fundamental challenge in the sixth-generation (6G) wireless communication, where extremely large antenna arrays (ELAA) enable near-field communication (NFC) but introduce significant signal processing complexity. Traditional model-based methods suffer from high computational costs and limited scalability in large-scale ELAA systems, while existing learning-based approaches often lack robustness across diverse channel conditions. To overcome these limitations, we propose the Residual Attention Convolutional Neural Network (RACNN), which integrates convolutional layers with self-attention mechanisms to enhance feature extraction by focusing on key regions within the CNN feature maps. Experimental results show that RACNN outperforms both traditional and learning-based methods, including XLCNet, across various scenarios, particularly in mixed far-field and near-field conditions. Notably, in these challenging settings, RACNN achieves a normalized mean square error (NMSE) of 4.8*10^(-3) at an SNR of 20dB, making it a promising solution for near-field channel estimation in 6G.