<p>The constitutive models of shape memory alloys (SMAs) play an important role in facilitating the widespread application of such types of alloys in various engineering fields. However, to accurately describe the deformation behaviors of SMAs, the concepts in classical plasticity are employed in the existing constitutive models, and a series of complex mathematical equations are involved. Such complexity brings inconvenience for the construction, implementation, and application of the constitutive models. To overcome these shortcomings, a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network (ANN). In the proposed model, the components of the strain tensor in principal space, ambient temperature, and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features, and the components of the stress tensor in principal space are set as the output. The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine. The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model, which is generated from the existing constitutive model (numerical experiments). To validate the capability of the proposed model, the predicted stress-strain responses of SMA material, and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments. This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.</p>