A new method is proposed for predicting the folding type of a protein according to its amino acid composition based on the following physical picture: (1) a protein is characterized as a vector of 20-dimensional space, in which its 20 components are defined by the compositions of its 20 amino acids; and (2) the similarity of two proteins is proportional to the mutual projection of their characterized vectors, and hence inversely proportional to the size of their correlation angle. Thus, the prediction is performed by calculating the correlation angles of the vector for the predicted protein with a set of standard vectors representing the norms of four protein folding types (i.e., all alpha, all beta, alpha + beta, and alpha/beta). In comparison with the existing methods, the new method has the merits of yielding a higher rate of correct prediction, displaying a more intuitive physical picture, and being convenient in application. For instance, in predicting the 64 proteins in the development set based on which the standard vectors are derived, the average accuracy rate is 83.6%, which is higher than that obtained for the same set of proteins by any of the existing methods. The average accuracy predicted for an independent set of 35 proteins of known X-ray structure is 91.4%, which is significantly higher than any of the reported accuracies so far, implying that the new method is of great value in practical application. All of these have demonstrated that the new method as proposed in this paper is characterized by an improved feature in both self-consistency and extrapolating-effectiveness.