[Objective] This paper proposes a new method for evaluating and classifying patent values. [Methods] With the help of value indicators, we designed a patent value analysis and classification system based on self-organizing maps (SOM) and support vector machine (SVM) techniques. We used the SOM to determine value categories, and then applied the random forest (RF) algorithm to rank value indictors based on their significance. Finally, we improved classification performance with the wrapped feature reduction method. [Results] The value tags determined by SOM effectively represented the patent values. Meanwhile, the value indictors were reduced from 14 to 10, and the classification accuracy was increased from 76.28% to 86.89%. [Limitations] Further refinement of patent values in each category is needed, which might reduce the patent value indicators. [Conclusions] The proposed SOM-RF-SVM method could support research and development activities as well as reduce the dependence on human factors.
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