Focusing on the issue that feature relevancy and dimension disaster problem in high-dimensional representation of Magnetic Resonance Imaging (MRI) prostate tumor Region of Interesting (ROI), a prostate tumor CAD model was proposed based on Neural Network (NN) with Principal Component Analysis (PCA) feature-level fusion in MRI. Firstly, 102 dimension features were extracted form MRI prostate tumor ROI, including 6 dimension geometry features, 6 dimension statistical features, 7 dimension Hu invariant moment features, 56 dimension GLCM texture features, 3 dimension Tamura texture features and 24 dimension frequency features. Secondly, 8 dimension features with cumulative contribution rate of 89.62% were obtained by using PCA in feature-level fusion, reducing the dimension of the feature vectors. Thirdly, the classical NN, which used Broyden-Fletcher-Goldfarb-Shanno (BFGS), Back-Propagation (BP) and Gradient Descent (GD), Levenberg-Marquardt as the training algorithm, was regarded as classifier to classify the features. Finally, 180 MRI images of prostate patients were used as original data, and the prostate tumor CAD model based on NN with feature-level fusion was utilized to diagnose. The experimental results illustrate that the ability to identify benign and malignant prostate tumor of neural network with PCA feature-level fusion is improved at least 10%, and the feature-level fusion strategy is effective, which increases the feature irrelevancy to a certain extent.