We propose a user-drawn sketch image-based three-dimensional (3D) object recognition method, which automatically learns and optimizes features by using unsupervised algorithm to overcome the difficulty of extracting robust features from the black and white sketch image. As a preprocessing task, both the sketch image database and the projected image database of the 3D objects are built by learning with various user-drawn sketch images and suggestive contour images of the 3D objects respectively, and each sketch image is mapped to the most similar projected database image by measuring the similarity. This enables us to avoid a direct comparison of the sketch query and the projected images of the 3D objects and to use the learned robust sparse features of the trained sketch images in the sketch database, compensating for the difference between the user-drawn sketch image and synthesized images of the 3D mesh model. The locally-enforced feature optimization of the local and global features of the database images reduces the error and retains the feature properties. Furthermore, we quantitatively compared the proposed method to previous remarkable object recognition approaches. Numerous experiments on various challenging 3D objects and sketch images demonstrate that the proposed methodology performs favorably against several state-of-the-art algorithms.