In recent years, with the improvement of the pace of life and the rapid development of the Internet, people are more inclined to communicate with the short text on many social platforms, and then some people can disturb the network's green environment by releasing the spam texts to hinder the normal social intercourse. In order to solve this problem, we propose a method of spam text detection based on optimized BP neural network and social platform. Through this method, the spam text filtering on the social platform is realized. First of all, through the stuttering participle and to stop word to construct keyword data set. Secondly, the keyword vector of the text expression is used to compute the weights of each keyword so as to reduce the dimension of the text vector and obtain the eigenvector. Finally, based on this, the BP neural network classifier is used to classify the short texts, and the spam text is detected and filtered. The experimental results show that with this method, the average classification accuracy for the 1000 dimensional text feature vector reaches 97.720%.