Figures Abstract Graph Neural Network (GNN) faces limitations in few-shot image classification due to insufficient adaptive feature extraction and limited long-range dependency modeling. To address these challenges, this study proposes an Improved Graph Neural Network (IGNN) integrating two key innovations. Firstly, we design an Attention-Enhanced Feature Extraction module, which combines Efficient Channel Attention (ECA) and self-attention mechanisms, enabling the model to dynamically focus on discriminative intra-image details and inter-image contextual relationships, thereby improving feature representation robustness. Secondly, we introduce a gated recurrent unit (GRU)-based Pre-message-passing mechanism, which establishes cross-sample associations between support and query sets before message propagation, effectively capturing long-range dependencies and mitigating information smoothing. The experimental results of three public datasets demonstrate that our proposed framework outperforms the existing methods and shows significant potential. It offers a pragmatic tool for applications requiring rapid adaptation to limited data, such as remote sensing and medical image analysis. Citation: Chen J, Fu B, Zou L (2026) IGNN: An improved graph neufral network with integrated attention and pre-message-passing for few-shot image classification. PLoS One 21(4): e0348057. https://doi.org/10.1371/journal.pone.0348057 Editor: Nagaraju Y, Dayananda Sagar College of Engineering, INDIA Received: June 14, 2025; Accepted: April 5, 2026; Published: April 28, 2026 Copyright: © 2026 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors used the publicly available datasets Omniglot dataset, MiniImageNet dataset, and CUB-200-2011 for the experiments. The Omniglot dataset can be accessed at https://github.com/brendenlake/omniglot. The MiniImageNet dataset can be accessed at https://image-net.org/update-mar-11-2021.php. The CUB-200-2011 dataset can be accessed at https://www.vision.caltech.edu/datasets/cub_200_2011/. Funding: This research was funded by the Sichuan Science and Technology Program, grant number 2025YFHZ0007, 2024JDHJ0015 and the Fundamental Research Funds for