How to find the spectra misclassified by traditional methods is the key problem that has been widely studied by the experts of astronomical data processing. We found that Isomap algorithm performs well for this problem. By comparing the performance of Isomap with that of principal component analysis (PCA), we found that (1) Isomap can project the spectra with similar features together and project the spectra with different features far away, while PCA may project the spectra with different features into nearby regions; (2) the outliers given by Isomap can be easily determined, and most of the outliers are binary stars with high scientific values; while the outliers given by PCA are difficult to determine and most of outliers are not binary stars. Thus, Isomap is more efficient than PCA in finding the outliers. Since the spectral data used in experiment are the spectra from the ninth data release of Sloan Digital Sky Survey (SDSS DR9), Isomap can find the spectra misclassified by SDSS pipeline efficiently and improve the classification accuracy obviously. Furthermore, since most of the spectra misclassified by SDSS pipeline are binary stars, Isomap can improve the efficiency of finding the binary stars with high scientific values. Though the experiment results show that Isomap is more sensitive to the noise than PCA, this disadvantage will not affect the application of Isomap in spectral classification since most of the spectra with low signal-to-noise ratios are the spectra whose spectral type cant be determined manually.