[Objective] This paper proposes a new method to predict technology convergence relationship and their values based on the patent classification network and text semantic features. [Methods] First, we calculated the correlation between patents and their classification to construct the co-occurrence network and obtain their structure similarity features. Then, we connected patent texts and their classification schema with the correlation strength. We also obtained the text semantic similarity features using text representation learning. Second, we constructed similarity indicators with the network structure and text semantic features, which were fused to create a feature vector. Third, we used the random forest model to learn the weights and contributions of different indicators and calculated the technology fusion probability. We also generated the candidate technology fusion relationship set. Fourth, based on the network characteristics and bibliometric characteristics of patent classification and citation, as well as their influence and potential growth, we created the evaluation indices for their technical, commercial and strategic values. Finally, we used the proposed method to evaluate the technology integration relationship. [Results] The accuracy of the proposed method is at least 20% higher than that of single feature prediction. In addition, the top 10 pairs of high-value technology convergence relations that identified by the proposed method have little difference with the real ranking result, in which the MAE is only 3.2. [Limitations] Some data sets are in-consistent, while more machine learning methods need to be utilized. [Conclusions] The feature convergence method has higher prediction accuracy than traditional methods. The proposed method can also effectively evaluate technology convergence relationship value.