当你使用MongoDB文本索引时，它会为每个匹配的文档生成一个分数。这个分数表示你的搜索字符串与文档匹配的程度。分数越高，与搜索到的文本相似的几率就越大。分数的计算方法是：

Step 1: Let the search text = S
Step 2: Break S into tokens (If you are not doing a Phrase search). Let's say T1, T2..Tn. Apply Stemming to each token
Step 3: For every search token, calculate score per index field of text index as follows:
       
score = (weight * data.freq * coeff * adjustment);
       
Where :
weight = user Defined Weight for any field. Default is 1 when no weight is specified
data.freq = how frequently the search token appeared in the text
coeff = ​(0.5 * data.count / numTokens) + 0.5
data.count = Number of matching token
numTokens = Total number of tokens in the text
adjustment = 1 (By default).If the search token is exactly equal to the document field then adjustment = 1.1
Step 4: Final score of document is calculated by adding all tokens scores per text index field
Total Score = score(T1) + score(T2) + .....score(Tn)

因此，正如我们可以看到上面的分数是由以下因素影响：
1.与实际搜索文本匹配的术语数，匹配越多，得分越高
1.文档字段中的令牌数
1.搜索的文本是否与文档字段完全匹配
以下是您的一个文档的派生：

Search String = This sentence have nothing to do with any other
Document = Who is the “He” in this sentence?

Score Calculation:
Step 1: Tokenize search string.Apply Stemming and remove stop words.
    Token 1: "sentence"
    Token 2: "nothing"
Step 2: For every search token obtained in Step 1, do steps 3-11:
        
      Step 3: Take Sample Document and Remove Stop Words
            Input Document:  Who is the “He” in this sentence?
            Document after stop word removal: "sentence"
      Step 4: Apply Stemming 
        Document in Step 3: "sentence"
        After Stemming : "sentence"
      Step 5: Calculate data.count per search token 
              data.count(sentence)= 1
              data.count(nothing)= 1
      Step 6: Calculate total number of token in document
              numTokens = 1
      Step 7: Calculate coefficient per search token
              coeff = ​(0.5 * data.count / numTokens) + 0.5
              coeff(sentence) =​ 0.5*(1/1) + 0.5 = 1.0
              coeff(nothing) =​ 0.5*(1/1) + 0.5 = 1.0    
      Step 8: Calculate adjustment per search token (Adjustment is 1 by default. If the search text match exactly with the raw document only then adjustment = 1.1)
              adjustment(sentence) = 1
              adjustment(nothing) =​ 1
      Step 9: weight of field (1 is default weight)
              weight = 1
      Step 10: Calculate frequency of search token in document (data.freq)
           For ever ith occurrence, the data frequency = 1/(2^i). All occurrences are summed.
            a. Data.freq(sentence)= 1/(2^0) = 1
            b. Data.freq(nothing)= 0
      Step 11: Calculate score per search token per field:
         score = (weight * data.freq * coeff * adjustment);
         score(sentence) = (1 * 1 * 1.0 * 1.0) = 1.0
         score(nothing) = (1 * 0 * 1.0 * 1.0) = 0
Step 12: Add individual score for every token of search string to get total score
Total score = score(sentence) + score(nothing) = 1.0 + 0.0 = 1.0

用同样的方法，你可以推导出另一个。
如需了解更多详细的MongoDB分析，请查看：Mongo Scoring Algorithm Blog

文本搜索为索引字段中包含搜索项的每个文档分配一个分数。分数确定文档与给定搜索查询的相关性。
对于文档中的每个索引字段，MongoDB将匹配的数量乘以权重，并将结果相加，然后使用这个和，MongoDB计算文档的得分。
索引字段的默认权重为1。
https://docs.mongodb.com/manual/tutorial/control-results-of-text-search/