This paper evaluated the potential application of big data technology to assessments of diminished ovarian reserve (DOR). The study enrolled 162 patients who underwent ovarian reserve function assessment for the first time in the Department of Ultrasound, Jiangsu Province Hospital of Chinese Medicine from January 2023 to December 2023. Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models—multilayer perceptron, one-vs-rest, and random forest classifiers—to classify and analyse the ovarian reserve function dataset and evaluate the platform’s performance. In the big data platform, the random forest algorithm achieved the highest classification accuracy (89.47%), followed by the neural network (81.06%) and support vector machine (72.91%) methods. The random forest algorithm had the least time overhead for datasets smaller than 50 MB; for datasets exceeding 50 MB, the support vector machine algorithm had the least time overhead, followed by the random forest and neural network algorithms. The neural network algorithm’s speedup ratio was lower than that of the other two algorithms for small datasets, but with increasing dataset size, its speedup ratio significantly exceeded those of the other two algorithms. The random forest algorithm showed substantial growth for large datasets.