The use of cloud-connected infrared spectroscopy technology combined with chemometrics to identify the rarest saffron and its commonly encountered adulterants (carthami flos, corn silk, nelumbinis stamen, chrysanthemi flos, pulp) and adulterated saffron, and quantitative determination of adulterant in saffron. Near-infrared spectra of saffron, adulterants, and adulterated saffron were collected by using the PV500R-I portable near-infrared instrument controlled by mobile phone. The first derivative, second derivative, third derivative, standard normal variable transformation and multiplicative scatter correction are used to preprocess the original spectral data. Partial Least Squares Discrimination Analysis was used to establish the identification model of saffron and its adulterant, and saffron and adulterated saffron. The results show that an optimal recognition model can distinguish saffron and its five kinds of adulterant from each other completely; the lowest 93% external prediction accuracy of saffron and five kinds of samples of adulterated saffron can be achieved step-by-step by two optimal recognition models, and the adulteration recognition level of saffron mixed with carthami flos, corn silk, nelumbinis stamen, chrysanthemi flos and pulp are 0.5%, 0.5%, 4.0%, 0.5% and 0.5%, respectively. Partial least squares regression was used to establish quantitative prediction models for the five kinds adulterant in saffron. The external prediction correlation coefficient range of the final model was 0.920~0.999, and RMSEP range was 0.005~0.044, and when the saffron mixed with carthami flos, chrysanthemi flos, nelumbinis stamen, pulp and corn silk are more than 8%, its external prediction relative error is less than 8%, 8%, 3%, 10% and 5% respectively, which indicated that the quantitative prediction model could be used to predict the amount of adulterant in saffron. To sum up, the identification method based on cloud connected portable near-infrared spectroscopy and the prediction method of the amount of adulterant is fast and accurate, economic and environmental protection, and can meet the requirements of quick and non-destructive identification of saffron on site.