Indoor light energy harvesting plays an important role in field of renewable energy. Indoor lighting condition is usually described by level of illumination. However, measured data alone does not by classification of different light sources, result is not representative. Energy harvesting system needs to be evaluated after classification to obtain more accurate value. This is also importance of different light source classification. In this thesis, a complete set of indoor light energy harvesting system is introduced, two models are proposed to evaluate energy, robustness is improved by mixing complex light condition during data collection. Main task of this thesis is to verify importance of indoor light classification. Main contribution of this thesis is to fill a gap in energy evaluation, and built a model with superior performance. In terms of collecting data, this thesis researches influence factor of data collection to ensure reliability of accuracy. This work can more accurately collect spectral under different light conditions. Finally, light energy is evaluated by classification of indoor light. This model is proven to be closer to true energy value under real condition. The result shows that classified data is more accurate than direct calculation of energy,it has a smaller error. In addition, performance of classifier model used in this thesis has been proven to be excellent, classifier model can still carry on high-accuracy classification when measurement data are not included in training data set. This makes it a low-cost alternative to measuring light condition without spectrometer.