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In order to ensure stable and reliable operation of equipment and reduce equipment failure, a method based on Support Vector Data Description (SVDD) is proposed to evaluate equipment health for the current situation of unbalanced equipment samples. The method firstly uses the normal health data of the device to conduct SVDD single class learning. Then using a small number of data samples of various health states to calculate the SVDD model hypersphere distance, combined with the health degree of its evaluation, using the binomial regression algorithm to obtain the fitness fitting curve, the health degree accurate evaluation model is realized. In the calculation process, all samples are added the dynamic weight processing of exponential variational weight to improve the accuracy. Finally, the test is verified by taking a radar transmitter as an example. The experimental results show that the method has good practical value for accurate assessment of equipment health status.