Visual-inertial odometry (VIO) achieves pose estimation by fusing visual and inertial data. In complex environments, inertial data are prone to noise interference, and long-term motion leads to cumulative errors. Additionally, most VIO models overlook local information interaction between modalities and fail to fully utilize their complementary nature, thereby compromising pose estimation accuracy. To address these issues, this study proposes an attention and local interaction-based visual-inertial odometry (ALVIO) model. First, the model extracts visual features and inertial features. Then, the historical time-series information of the inertial features is preserved, and a channel attention mechanism based on discrete cosine transform (DCT) is applied to enhance low-frequency effective features and suppress high-frequency noise. Next, a multi-modal local interaction and global fusion module is designed, which gradually achieves local interaction and global fusion between modalities through improved split-attention mechanism and MLP-Mixer. This module adjusts the local feature weights based on the contributions of different modalities to realize inter-modal complementarity and then integrates the features globally to obtain a unified representation. Finally, the fused features are used for temporal modeling and pose regression to obtain relative poses. To verify the effectiveness of the model in complex environments, this paper conducts experiments on processed low-quality versions of the public KITTI and EuRoC datasets. The results show that, compared to the direct feature concatenation model, the multi-head attention fusion model, and the soft mask fusion model, ALVIO reduces the translation error by 49.92%, 32.82%, and 37.74%, respectively, and the rotation error by 51.34%, 25.96%, and 29.54%, respectively, while also demonstrating higher efficiency and robustness.