Autoencoder network (AN) is a nonlinear dimension reduction manifold learning algorithm which can find out nonlinear low-dimensional manifold structure from high dimensional spectra data effectively. In the present paper, a nonlinear infrared (IR) spectra modeling method AN-PLS was proposed by combining AN and partial least squares (PLS) to reflect the nonlinear correlations existing between IR spectra and physicochemical properties of samples. In AN-PLS, AN and PLS were adopted to deduct the dimensions of IR spectra and build regression calibration model, respectively. The AN-PLS was then applied to correlate the near infrared (NIR) spectra and the mid infrared (MIR) spectra with the concentrations of insoluble dietary fiber in bamboo shoots. The results indicate that AN-PLS can predict the concentrations of insoluble dietary fiber in bamboo shoots with a lower cross validation RMS error (RMSECV) and higher determinative coefficient (R2), than other common spectra data preprocessing methods combined with PLS or sole PLS. It can be concluded that AN-PLS can effectively model the nonlinear correlations between IR spectra and physicochemical properties of the samples. And it is feasible to accurately detect the concentrations of insoluble dietary fiber in the bamboo shoots by coupling NIR and MIR spectra with AN-PLS modeling method.