To solve the problem of a low classification accuracy of single classifier in forest classification, a classification model (ACO-SVM) that combines the improved ant colony algorithm (ACO) with support vector machine (SVM) is proposed. In the improved algorithm, a partial finite search was introduced into the ant colony search to avoid local extrema. A time-varying function was introduced into pheromone updating. The dynamic update policy was combined with SVM to optimize the parameters of the radial basis kernel function. The proposed model was verified using an experiment based on the classification of the forest types using UAV visible remote sensing images. In the spectral feature image classification, compared with ABC-SVM, GA-SVM, and conventional SVM models, the proposed ACO-SVM achieved the best forest-type classification performance, with an overall classification accuracy of 81% and a Kappa coefficient of 0.7500. After introducing different textural features, the classification was performed for the Genhe forest area in the Greater Khingan Mountains based on the grayscale co-occurrence matrix feature, and the proposed ACO-SVM showed an overall classification accuracy of 85% and a Kappa coefficient of 0.8063. After introducing the Gabor textural feature, ACO-SVM achieved the overall classification accuracy and Kappa coefficient of 87.5% and 0.8438, respectively.