Objective To investigate the monitoring efficacy of the BigThumb^® electrocardiogram (ECG) monitor for the recurrence of atrial fibrillation (AF) after ablation and the advantages of artificial intelligence algorithm in the diagnosis of AF. Methods A total of 185 eligible patients with nonvalvular AF who underwent interventional ablation in our hospital from Mar. 2019 to Aug. 2019 were prospectively selected and randomly divided into two groups:BigThumb^® group (BT group, n=94) and traditional follow-up group (TF group, n=91). The AF-free survival rates of the two groups were followed up and compared, and the sensitivity and specificity of artificial intelligence algorithm and traditional ECG algorithm in the diagnosis of AF were also analyzed. Results The patients in BT group and TF group were followed up for 98-263 (157.0±38.4) d and 91-268 (158.8±54.7) d, respectively. Log-rank test results showed that the AF-free survival rates were 79.8% (75/94) and 92.3% (84/91), respectively (P<0.05). Three months after ablation the proportion of patients who changed treatment strategy in BT group was higher than that in TF group (P<0.05). A total of 18 981 ECGs were recorded in BT group, of which 1 520 (8.0%) were diagnosed as AF by electrocardiographists. The sensitivity and specificity of the artificial intelligence algorithm were 96.5% (1 467/1 520) and 99.6% (17 391/17 461), respectively, which were significantly higher than those of the traditional ECG algorithm (89.7%[1 363/1 520] and 97.2%[16 972/17 461]), respectively (both P<0.05). Conclusion The BigThumb^® ECG monitor improves the detection of AF recurrence after ablation, which is helpful to change the treatment strategy in time. Artificial intelligence algorithm can improve the accuracy of AF diagnosis and the BigThumb^® ECG monitor based on this algorithm is reliable in the follow-up for AF after ablation.