The intelligent indoor localization based on WIFI is increasingly concerned for its universality. However, in practical applications, its indoor localization accuracy is limited by noises, diffractions and multipath effects. To overcome these drawbacks, we design a new intelligence indoor localization system based on Channel State Information (CSI) of the wireless signal from Multiple Input Multiple Output (MIMO), named IILC. In IILC, the initial amplitude information is first processed in the measured CSI data, which can effectively suppress the impact from noise and other interference. Next, we explore a method to construct radio image. It can make full use of space-frequency information and time-frequency information from CSI-MIMO to obtain more location information. Then, we design a new deep learning network to obtain the optimal effective of radio image classification. Moreover, a mixed-norm is proposed to impose sparsity penalty and overfit constraint on the loss function, which makes the valuable feature units active and the others inactive. The experimental results verify that IILC system has excellent performance. The overall localization accuracy of IILC in the office scene can reach 97.10%, and the probability of localization error within 1.2m is 86.21%.