Forest plant identification and classification play a pivotal role in various domains, encompassing biodiversity conservation, agricultural advancement, and beyond. Conventional plant identification methods often rely on expert botanists or manual identification approaches, which can be time-consuming and subjective. Deep learning models have emerged as a promising approach to automatically classify plants, offering high accuracy and efficiency. However, these models often rely on convolutional neural networks (CNNs) and their variants to extract features, which may fail to capture the complex relationships among plant characteristics. This paper proposes a novel feature extraction method using semi-supervised learning techniques combined with Masked Autoencoder architecture to enhance the feature extraction of plant data, applicable to problems with limited datasets. The proposed model, named MAE SGD, achieves an accuracy of nearly 94% on the QuangNamForestPlant - a dataset collected by our research team in Quang Nam province, Central Vietnam, comprising 24,314 images of 710 different forest plant species. Future research directions will focus on expanding the forest plant dataset and improving the recognition model to increase the model’s accuracy and overall performance in identifying forest vegetation.