The reciprocating water injection pump is the core equipment for the oilfield water injection operations. However, due to the harsh working environment, it is easy to cause frequent failures of bearings, cylinders, motors, and other components. Therefore, the health monitoring and the health evaluation of the reciprocating water injection pump are required to detect abnormal conditions in a timely manner to ensure safety and reliability for reciprocating water injection operation. This paper proposed a quantitative evaluation method for the health of the reciprocating water injection pump based on support vector data description. Firstly, considering the nonlinear and non-stationary characteristics of the reciprocating water injection pump vibration signal, the characteristic information in the water injection pump vibration signal was extracted under limited conditions. Aiming at the problem that the water injection pump had many core components and small spacing, the measurement point locations of the vibration signals were analyzed to build a data acquisition system. In view of the complex frequency components of vibration signals, variational modal decomposition was used to extract the variational modal components of vibration data, and a high-dimensional multi-domain feature set was constructed based on the permutation entropy of all modal components to describe the health status of the water injection pump. Secondly, in view of the fact that during the actual data collection process of the water injection pump, most of the equipment was in a healthy working state and had less fault data, which easily caused sample imbalance. The single value classification method was used to support the advantages of vector data description in single value classification problems. Only the eigenvectors of the water injection pump data samples running in the healthy state were used to construct the hypersphere model described by the support vector data, and the particle swarm optimization algorithm was introduced to optimize the model parameters; the data of the different health states of the water injection pump were calculated to the hypersphere model distance from the center, and perform formula fitting with reference to the membership function to achieve quantitative evaluation of the health of the water injection pump. Finally, in order to verify the applicability of this evaluation method, the health evaluation calculation was performed on the vibration data of the on-site water injection pump in Changqing Oilfield. After particle swarm optimization, the model accuracy could reach 95%, which showed that the method proposed in this article has good practicability.