from wordcloud import WordCloud

 string = 'Importance of relative word frequencies for font-size. With relative_scaling=0, only word-ranks are considered. With relative_scaling=1, a word that is twice as frequent will have twice the size. If you want to consider the word frequencies and not only their rank, relative_scaling around .5 often looks good.'

 font = r'C:\Windows\Fonts\FZSTK.TTF'

 wc = WordCloud(font_path=font, #如果是中文必须要添加这个，否则会显示成框框

                background_color='white',

                width=1000,

                height=800,

                ).generate(string)

 wc.to_file('ss.png') #保存图片

 import jieba

 cut = jieba.cut(text)  #text为你需要分词的字符串/句子

 string = ' '.join(cut)  #将分开的词用空格连接

 import jieba

 from wordcloud import WordCloud

 from PIL import Image

 import numpy as np

 font = 'hwkt.ttf'

 content = (open('岗位需求.txt','r',encoding='utf-8')).read()

 cut = jieba.cut(content)

 cut_content = ' '.join(cut)

 img = Image.open('22.png') # 以什么图片进行显示

 img_array = np.array(img) # 将图片转换为数组

 wc = WordCloud(

     background_color='white',

     mask=img_array, # 若没有该项，则生成默认图片

     font_path=font # 中文分词必须有中文字体设置

 )

 wc.generate_from_text(cut_content) # 绘制图片

 wc.to_file('new.png') # 保存图片