Atrial fibrillation (AF) is the most common arrhythmia. Since AF can cause strokes if it lasts for a long time, it is important to detect AF in advance and receive treatment. Electrocardiography is usually used for AF diagnosis. Electrocardiography records the electrical activity of the patient’s heart to obtain an electrocardiogram (ECG), which usually consists of waves and a commentary on them. The onset of AF occurrence or its likelihood is judged by a comprehensive analysis of an ECG, which requires considerable prior knowledge and clinical experience. In this study, to make this process simpler, the output text of ECGs is analyzed by deep learning to predict the possibility of future AF. The proposed model represents words as vectors using FastText and extracts features using one-dimensional convolutional neural networks (CNNs). The model also combines features using global average pooling (GAP) and is trained to calculate the probability of developing AF. In an experiment, the model showed 85.03% accuracy in predicting the presence or absence of AF. We thus demonstrated the possibility of predicting the occurrence of AF in advance using only text analysis without prior knowledge and clinical experience of AF.