Thanks to the rapid development of deep learning, big data analysis
technology is not only widely used in the field of natural language processing,
but also more mature in the field of numerical prediction. It is of great
significance for the subject heat prediction and analysis of science and
technology demand data. How to apply theme features to accurately predict the
theme heat of science and technology demand is the core to solve this problem.
In this paper, a prediction method of subject heat of science and technology
demand based on time convolution network (TCN) is proposed to obtain the
subject feature representation of science and technology demand. Time series
prediction is carried out based on TCN network and self attention mechanism,
which increases the accuracy of subject heat prediction of science and
technology demand data Experiments show that the prediction accuracy of this
algorithm is better than other time series prediction methods on the real
science and technology demand datasets.

本文提出了一种基于时间卷积网络（TCN）的科技需求主题热度预测方法，利用主题特征进行科技需求主题热度的时间序列预测，通过实验结果表明该算法在真实科技需求数据集上的预测准确度优于其他时间序列预测方法。