This study applies facial recognition, human eye recognition, and speech recognition technology to consumer population analysis, and proposes a system that can collect consumer data in multiple dimensions and conduct intelligent product recommendation. Different from traditional data collection method, this system collects implicit evaluation data and collects explicit evaluation data in the meantime, which can effectively increase credibility of collected data. The system exploits on facial recognition to obtain consumers' facial features, makes use of human eye recognition to track down duration of human eyes staying and gazing at products, uses speech recognition to obtain sentiment polarity of texts and evaluation keywords, and uses a recommendation model based on user face attributes to recommend products. Through the experiments, we discover that 80% of the experimenters express their satisfaction with the first five recommended products introduced by the system, which was formerly trained by 20 other experimenters, showing that multi-dimensional collection of consumer data can break the limits of traditional data collection and possesses a higher credibility.