Prediction of protein folding rate is one of the most important challenges in contemporary biophysics. Over the past few years, many researchers have devoted great efforts to reveal the major determinants of protein folding rate, and many parameters and methods have been proposed successively. However, the interaction of amino acids and the sequence order information have never been considered as a property for predicting protein folding rates. It was proposed a novel method, which adopted Chou's pseudo-amino acid composition to extract the sequence order information, used Monte Carlo method to choose the optimal feature factors, and established the linear regression model to predict the protein folding rate. This novel method can predict protein folding rate from amino acid sequence without any knowledge of the tertiary or secondary structure, or structural class information. Using the Jackknife cross validation test, for the largest dataset yet studied including 99 proteins, it was found that the predicted folding rates correlated well with the experimental values; the correlation coefficient is 0.81, and the standard error is 2.54. The prediction quality is excelled with most existing sequence-based methods. The result implies that the sequence order information plays an important role in protein folding.