In order to solve the problems of feature extraction and calibration modelling in the area of quantitatively infrared spectra analysis, an input layer self-constructive neural network (ILSC-NN) is proposed. Before the NN training process, the training data is firstly analyzed and some prior knowledge about the problem is obtained. During the training process, the number of the input neurons is determined adaptively based on the prior knowledge. Meantime, the network parameters are also determined. This algorithm of the NN model helps to increase the efficiency of calibration modelling. The test experiment of quantitative analysis using simulated spectral data showed that this modelling method could not only achieve efficient wavelength selection, but also remarkably reduce the random and non-linear noises.