Abstract: In order to improve the prediction accuracy of protein secondary structure, quantum computation and multiple population genetic algorithms were added to the traditional genetic neural network algorithm. At the same time, the chemical and physical properties of amino acid residues was the main driving force to form protein secondary structure, and the conformational preference also greatly affected the formation of protein secondary structure, so a new coding method based on physical and chemical properties and conformation information was presented. This coding method which contains rich biological information could effectively reduce the uncertainty of the network system. This model was employed to predict 24 non homologous protein sequences in PDBSelect25. The result showed that this proposed model improved the prediction accuracy to 72.10%, increasing the prediction accuracy by 7.80%, 3.70% and 3.41% respectively compared with SNN, DSC and PREDSATOR method. In this new method, based on mixed coding and quantum computation, the multiple population genetic algorithms drove the quantum computation and the quantum computation feed back the multiple population genetic algorithms. This kind of drive and feed back mechanism could effectively improve the prediction accuracy of protein secondary structure.