Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. It also provides crucial information about the functionality of the proteins. Despite all the efforts that have been made during the past two decades, finding an accurate and fast computational approach to solve PFR still remains a challenging problem for bioinformatics and computational biology. It has been shown that extracting features which contain significant local and global discriminatory information plays a key role in addressing this problem. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in Position Specific Scoring Matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a Support Vector Machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy to 7.4% over the best results reported in the literature.