Determining the status of egg fertilization plays a major role in determining the quality of eggs and their products. In this regard, in order to achieve greater productivity and production, egg evaluation is considered necessary and important in terms of spermatogenesis. In this regard, spectroscopy was performed in the range of 0.01900 nm from 130 local egg samples in the direction of the main diameter for 3 days during the storage period. Spectrum data from spectrometers, in addition to sample information, include unwanted information and noise.  For this reason, in order to achieve accurate classification models, it is necessary to process spectral data before developing the appropriate model. In this regard, intelligent neural network classification was developed based on reference measurements and information of pre-processed spectra by combining different methods of smoothing, normalizing and increasing spectral separation power to determine the presence of sperm in the egg.  Classification results on day zero, first, second, warehousing with 72.3% accuracy, 73.1%, 75.5%, and detection, 86.31, 87.1%, 76% and sensitivity, respectively: 83 61%, 79.63% and 73.3% were obtained.