Research on the Application of Innovative Thinking in Secretarial Writing Practice

When doing secretarial writing, innovative thinking is even more crucial to write articles that can communicate with supervisors and subordinates and have excellent communication effects. In this paper, we first design the pre-trained word vector algorithm to project all the word vectors into the same semantic space, so that there is a distance relationship metric between words. Then a long and short-term memory network is introduced for processing the text, and a bi-directional LSTM network structure is used to do the segmentation of the secretary writing text, realizing the construction of the innovative thinking scoring model of secretary writing based on machine learning. Then the model is used to analyze the innovativeness of secretarial writing of two types of volunteers: public office affairs secretaries and business affairs secretaries, and finally a controlled test of innovative thinking to improve secretarial writing is conducted. It was found that the three groups of public office type affairs secretaries were 2.21%, 3.89%, 17.65% and 7.98%, 10.19%, 16.46% in the percentage of grammatical rhetoric and expressive skill innovations, respectively, and the high level group had more innovative content in grammatical rhetoric and expressive skill than the remaining two groups. The degree of innovativeness in writing theme intention of business secretaries is 9.32%, 16.29% and 29.75%, and business secretaries can be more flexible and free to use innovative thinking in the selection of writing themes. They have more innovative thinking in the selection of writing topics than the public service business secretaries. This study provides a new method to improve innovative thinking in secretarial writing, and to improve the efficiency and writing quality of secretarial writing.