Turbulence remains one of the most challenging and fundamental open problems in classical physics. Despite the apparent simplicity of the governing Navier–Stokes equations, their nonlinear and multiscale nature gives rise to complex behaviors making analytical treatment extremely difficult. The goal of this work is to assess the capability of machine learning to identify and interpret the interaction structure underlying the energy cascade in turbulence. Shell models are systems of coupled ordinary differential equations designed to mimic the energy cascade of turbulent system across discretized scales, and thus with a reduced number of degrees of freedom, while successfully reproducing many statistical features of fully developed turbulence.