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In recent years, the scale of intellectual property' rights financing has gradually expanded, and how commercial banks can predict risks objectively and accurately is especially important. To further study the risk-alert of intellectual property rights financing, this paper, from the perspective of commercial banks and based on the characteristics of Innovative Small and Medium-Sized Enterprises (SMEs), obtained 84 innovative SMEs data through Field research of Science and Technology Finance department of Tianjin Commercial Bank. The author measured the importance of indicators by the Gini descent method selected 14 indicators that most affected the financing risk out of the 54 indicators, and then used the random forest to establish an innovative SMEs intellectual property right financing risk-alert model, compared with the neural network and particle swarm optimization support vector machine accuracy and found that the stability and accuracy of random forest model are higher than the other two models. [ABSTRACT FROM AUTHOR]