When calculating text similarity, each file in the folder needs to be calculated separately

I use Jaccard to calculate the text similarity, hoping that the code can traverse each article in the folder, that is, get the similarity between each article and the reference document. But there is a problem with traversing the folder

import jieba 
import jieba.posseg as psg
import codecs
#将不用拆分的词组载入jieba
jieba.load_userdict("D:\IEdownload\毕设\代码\非拆分.txt")
#定义转化为字符串函数
def standardization(filename):
    data=''
    with open(filename,'r',encoding='utf-8') as f:
        for line in f.readlines():
            line=line.strip('\n')
            data+=line
        return data
#定义jaccard相似度函数
def Jaccard(model,reference):
    terms_reference=jieba.cut(reference)
    terms_model=jieba.cut(model)
    grams_reference=set(terms_reference)
    grams_model=set(terms_model)
    temp=0
    for i in grams_reference:
        if i in grams_model:
            temp=temp+1
    fenmu=len(grams_model)+len(grams_reference)-temp
    jaccard_coefficient=float(temp/fenmu)
    return jaccard_coefficient

#输入参照文档
query="D:\IEdownload\毕设\代码\参考文档.txt"

Here are the target document traversal methods I'm trying so far

import os
import re
path = "D:\IEdownload\毕设\新闻数据\保险集团\中国人寿1" #文件夹目录
files= os.listdir(path) #得到文件夹下的所有文件名称
for file in files: #遍历文件夹
    position = path+'\\'+ file #构造绝对路径
    with open(position, "r",encoding='gb18030') as f:    #打开文件
        data = f.read()   #读取文件
        #print("--------------------------")
        #print(data)
        data_new=re.sub('\s','',data)
        n=standardization(data_new)
        print(Jaccard(m,n))

Thanks!!