This paper improvesan analysis model ofstock text sentiment orientation based on stock emotion dictionary and LDA. For the problem of the incomplete stock dictionary and the unilateral analysing of sentence, this paper constructs a relatively complete stock emotional dictionary, and analyses the emotional tendency of stock text from the three aspects of the tendency, the degree and the correlation of sentence. This paper builds a more complete emotional dictionary by introducing the stock words,the degree words and the turning words. Then it extracts the sentiment words from stock text sentences after the processing. It educes the sentence tendency and degree vector from the emotional words in the sentence by the sentence algorithm, and uses SVM and K mean algorithm to classify sentence vector. The paper gets the words distribution of the topic and the topic distribution of document from the sentiment words by LDA model, and obtains the correlation of the sentence by this probability distribution. Finally it synthesizes sentence tendency, degree, correlation to obtain the sentence emotion, and acquires the emotional tendency of stock text through the sentence emotion. At last, This paper collects the text of Baidu news in the sector of the economy as the experimental material, and does experiment and compares with other algorithms. Experimental results show that the accuracy of sentence and article classification are 82.78% and 84.14%.