Aiming at the problem of equipment life prediction with insufficient sample size and unbalanced distribution of small sample data, a joint optimization model based on improved smote algorithm and improved KNN (K-Nearest Neighbor) algorithm was constructed. First, the noise scale factor β was set to eliminate the noise in the sample data. Then, an improved smote (ISMOTE) was built through the combination of B-SMOTE (Borderline-SMOTE) algorithm and traditional SMOTE algorithm to add and optimize a few samples with distribution problems, so as to avoid the deviation caused by unbalanced sample distribution and small number of samples. Secondly, for the sample points with fuzzy boundary in the classification process, the particle swarm optimization algorithm was used to find the center point of each sample type and calculate the mean value of sample distance to establish the separation threshold $\bar d $. For the sample points within the threshold range, the "voting method" was used to judge the sample type, so as to avoid the error caused by the mixing of different kinds of samples in the KNN algorithm. Finally, through the simulation using the state data of caterpillar hydraulic pump and the vibration data of hydraulic guide bearing of Lingjintan hydropower station, the numerical examples show that the above two improved algorithms can accurately analyze the equipment operation state and predict the future healthy development trend of equipment in the face of small sample unbalanced equipment data.