Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost. However, the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift, highlighting the need for effective calibration methods to improve data reliability. This study proposes a data correction method based on Bayesian Optimization Support Vector Regression (BO-SVR), which combines the nonlinear modeling capability of Support Vector Regression (SVR) with the efficient global hyperparameter search of Bayesian Optimization. By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy, the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction. Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR, grid search SVR, and random forest (RF) models across multiple pollutants, including PM_2.5, PM₁₀, CO, NO₂, SO₂, and O₃. The proposed method achieves lower prediction residuals, higher fitting accuracy, and better generalization, offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.