Data mining is a technique for obtaining useful information from vast amounts of information. Big data refers to large amounts of complicated information that is processed, particularly in relation to biological processes. The investigation of protein structures has recently received a lot of attention from structural biologists. The majority of recent research projects have tried to improve protein structure identification in huge data. Feature selection-based protein structure identification in large data analysis, on the other hand, takes a long time. A hybrid crow search algorithm and particle swarm optimization (CSA-PSO) based CD4.5 (CP-CD) approach has been developed to increase Protein Structure Identification accuracy with less amount of time. First samples from the patients are given to IOT-enabled microscope and the details will be stored in big data and then the process will be divided into two steps. At first, feature selection is done using CSA-PSO algorithm, and the classification is done using CD4.5 classifier. This aids in identifying the protein structure and accurately diagnosing the condition, as well as lowering the false positive rate.