Abstract Background For ischaemic stroke patients with gastrointestinal haemorrhage, stopping antiplatelet drugs or reducing the dose of antiplatelet drugs was a conventional clinical therapy method. But not a study to prove which way was better. And the machinery learning methods could help to obtain which way more suit for some patients. Methods Data from consecutive ischaemic stroke patients with gastrointestinal haemorrhage were prospectively collected. The outcome was a recurrent stroke rate, haemorrhage events, mortality and favourable functional outcome (FFO). We analysed the data using conventional logistic regression methods and a supervised machine learning model. We used unsupervised machine learning to group and analyse data characters. Results The patients of stopping antiplatelet drugs had a lower rate of bleeding events (p = 0.125), mortality (p = 0.008), rate of recurrence of stroke (p = 0.161) and distribution of severe patients (mRS 3–6) (p = 0.056). For Logistic regression, stopping antiplatelet drugs (OR = 2.826, p = 0.030) was related to lower mortality. The stopping antiplatelet drugs in the supervised machine learning model related to mortality (AUC = 0.95) and FFO (AUC = 0.82). For group by unsupervised machine learning, the patients of better prognosis had more male (p