  We present a novel deep learning method for computing eigenvalues of the
fractional Schr\"odinger operator. Our approach combines a newly developed loss
function with an innovative neural network architecture that incorporates prior
knowledge of the problem. These improvements enable our method to handle both
high-dimensional problems and problems posed on irregular bounded domains. We
successfully compute up to the first 30 eigenvalues for various fractional
Schr\"odinger operators. As an application, we share a conjecture to the
fractional order isospectral problem that has not yet been studied.
