Alzheimer's disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person's ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer's disease.