Proteins are considered to be one of the most important biological macromolecules and play a wide range of vital roles in most biological interactions. Therefore, determining how they function is an important task in biology and biomedical science. Protein fold recognition is defined as assigning a given protein to a fold (among a finite number of folds) that represents its functionality as well as its major tertiary structure. Despite all the efforts made over the last two decades, finding an effective computational approach to solve this problem still remains challenging for computational biology and bioinformatics. In this study we enhance the protein fold prediction accuracy by employing evolutionary and structural information for feature extraction. Based on our previously proposed feature extraction techniques to extract physicochemical-based features, we develop segmented distribution and segmented auto-covariance feature extraction methods to extract local evolutionary and structural information. By applying an SVM to our extracted features, we enhance the protein fold prediction accuracy up to 7.2% better than the best result found in the literature. In conclusion, we develop several segmentation-based feature extraction techniques that enable us to extract local discriminatory information for protein fold recognition better than previously proposed approaches to achieve this goal.