Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an important clinical risk factor resulting in AD, genomic studies, such as genome-wide association studies (GWAS), have widely been applied into AD studies. However, main shortcomings of GWAS method were that hereditary deletions were evident in the GWAS studies, which resulted in low classification or prediction abilities by using GWAS analysis. Therefore, this paper proposed a novel deep learning genomics approach and applied it to discriminate AD patients and healthy control (HC) subjects. In this study, we selected genotype data of 988 subjects enrolled in the ADNI, including 622 AD patients and 366 HC subjects. The proposed deep learning genomics (DLG) approach was composed of three steps: quality control, SNP genotype coding, and classification. The Resnet framework was used as the DLG model in this study. In the comparative GWAS analysis, APOE ε4 status and the normalized theta-value of the significant SNP loci were seen as predictors to classify genetically using Support Vector Machine (SVM). All data were divided into one training & validation group and one test group. 5-fold cross-validation was used in 500 times. Finally, we compared the classification results between DLG model and traditional GWAS analysis. As a result, the accuracy, sensitivity, and specificity of classification for traditional GWAS analysis was 71.38%±0.63%, 63.13%±2.87% and 85.59%±6.66% in the test group; while the accuracy, sensitivity, and specificity of classification for DLG model was 92.65%±4.80%, 85.00%±16.25% and 97.10%±4.38% in the test group. Hence, the DLG model can achieve higher accuracy and sensitivity when applied to AD. More importantly, we discovered several novel genetic biomarkers of AD, including rs6311 and rs6313 in HTR2A, and rs690705 in RFC3. The roles of these novel loci in AD should be explored future.