Concerning the low accuracy of global Chinese microblog sentiment classification, a new model was introduced from the perspective of Multi-dimensional Topics based on Long Short-Term Memory (MT-LSTM). The proposed model was constituted by hierarchical multidimensional sequence computation, it was composed of Long Short-Term Memory (LSTM) cell network and suitable for processing vector, array and higher dimensional data. Firstly, microblog was divided into multiple levels for analysis. To upward spread, sentiment tendencies of words and phrases were analyzed by three-Dimensional Long Short-Term Memory (3D-LSTM); to rightward spread, sentiment tendencies of the whole microblog were analyzed by Multi-Dimensional Long Short-Term Memory (MD-LSTM). Secondly, sentiment tendencies were analyzed by Gaussian distribution in topic sign. Finally, the classification result was obtained by weighting above analyses. The experimental results show that the average precision of the proposed model reached 91%, up to 96.5%, and the recall of the neutral microblog reached 50%. In the comparison experiments with Recursive Neural Network (RNN) model, the F-measure of MT-LSTM was enhanced above 40%; compared with no topic division, the F-measure of MT-LSTM was enhanced by 11.9% because of meticulous topic division. The proposed model has good overall performance, it can effectively improve the accuracy of analyzing Chinese microblog sentiment tendencies and reduce the amount of training data and the complexity of matching calculation.